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REVIEW 5 major objections 5 minor 98 references

LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read LineArt transfers complex materials and lighting onto detailed design drawings with a training-free diffusion pipeline that outperforms current state-of-the-art methods in accuracy, realism, and material precision.

desk verdict A useful training-free appearance transfer pipeline with a valuable new dataset, but the evaluation hygiene and a circular soft-edge bootstrap keep the central claim from being fully supported. read the letter →

arxiv 2412.11519 v1 pith:VAYYLS4W submitted 2024-12-16 cs.CV

Xi Wang , Hongzhen Li , Heng Fang , Yichen Peng , Haoran Xie , Xi Yang , Chuntao Li This is my paper · ORCID
classification cs.CV
keywords LineArtappearancetransferdesigndrawingsdiffusionmodeltraining-freemulti-frequencylinesfusionRetinexProdataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

LineArt is a training-free framework for transferring the appearance of a reference photo — its materials, textures, colors, and lighting — onto a professional line drawing while keeping the drawing's fine structure intact. The paper argues that text prompts and existing image-translation methods fail on detailed design drawings, and that the right way is to guide a pretrained diffusion model with structure cues decomposed into three edge levels and with appearance cues split into a lighting stage and a texture stage. If the claim holds, designers can render technical drawings with realistic materials without 3D modeling, physical property specifications, or paired training data. The paper also introduces ProLines, a professional design drawing dataset, to evaluate the method.

What carries the argument

The framework's two engines are the multi-frequency lines fusion module and the two-part painting process. The first decomposes the input line drawing into three levels of edge assertions — a single-line outer contour from the mask, double lines from morphological dilation-erosion, and soft edges extracted via Haar filtering of an initial depth-guided generation — which are fused and fed to ControlNet as geometric conditioning. The second splits appearance transfer: Base Layer Shaping computes a multi-scale Retinex illumination image, extracts its brightness mean $L_{\text{mean}}$, and shifts the initial latent noise by $x'_0 = L_{\text{mean}} + (x_0 - L_{\text{mean}}) \times 0.5$ to pre-bias lighting, while Surface Layer Coloring removes the reference background, reassembles the remaining pixels into a synthetic texture image, and injects its embedding into specific U-net attention layers to control material and color.

What would settle it

Run a controlled sweep: fix one line drawing and one seed, vary only the reference photo's brightness (or its Retinex-derived $L_{\text{mean}}$) across a wide range, and measure whether the generated image's mean illumination tracks the reference's $L_{\text{mean}}$ monotonically. If output brightness is insensitive to $L_{\text{mean}}$, or is non-monotonic, the core mechanism of Base Layer Shaping is falsified; a complementary check replaces the shifted latent $x'_0$ by the unshifted $x_0$ and compares lighting-aware metrics.

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Extended reading notes

Core claim

The central discovery is that a knowledge-guided, fully training-free pipeline built on ControlNet, IP-Adapter, and Stable Diffusion can outperform state-of-the-art appearance-transfer and sketch-to-image methods specifically on detailed, professional design drawings. By decomposing the input drawing into single lines, double lines, and soft edges, and by separating the colorization into Base Layer Shaping (driving the initial latent with a brightness analysis of the reference's Retinex illumination) and Surface Layer Coloring (injecting a reassembled texture map into chosen U-net attention layers), the method claims high fidelity to both the drawing's structure and the reference's material, with quantitative gains on eight metrics and user-study wins across edge fidelity, appearance, and overall perception.

Load-bearing premise

The method assumes that shifting the initial latent noise by a pixel-domain brightness statistic of the reference photo transfers the reference's lighting to the final image; this mapping is asserted rather than derived, and the 0.5 scale is chosen by hand, so if that linkage is wrong the Base Layer Shaping stage does not do its claimed work.

Editorial extensions

If this is right

  • Designers can preview materials on technical drawings without any training or 3D modeling, needing only a line drawing and a reference photo.
  • The three-level line decomposition gives control over pattern thickness and texture density, so the same framework adapts to different drawing complexities.
  • Separating lighting from texture avoids the attribute degradation that appears when a single text prompt or image prompt tries to convey both at once.
  • The ProLines dataset provides a professional-drawing benchmark for future sketch-to-image and appearance-transfer research.
  • Because the pipeline is training-free, it can be rebuilt on any future diffusion backbone or adapter with the same structure and appearance guidance modules.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The pixel-space-to-latent-space brightness transfer used in Base Layer Shaping suggests a testable extension: replacing the hand-set 0.5 factor with a calibrated or per-image adaptive scale could make lighting control more precise and less dependent on the reference photo's dynamic range.
  • Because the method is assembled from modular components, an ablation that swaps the Retinex brightness analysis for a simple average brightness, or the patch reassembly for plain cropping, would reveal how much of the measured gain comes from the knowledge-guided decomposition versus the base model itself.
  • The same hierarchical edge decomposition could serve neighboring tasks, such as line-art colorization, sketch-based editing, or material preview in product design, wherever preserving fine structural detail is the bottleneck.
  • A direct stress test would apply the method to drawings with heavy pre-existing shading or dense cross-hatching, since the paper states these are failure cases and the framework's structure-preservation claims are most at risk there.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. LineArt proposes a training-free pipeline for transferring appearance from a reference photo to a detailed line drawing. It builds on Stable Diffusion, ControlNet, and IP-Adapter, and consists of two stages: a multi-frequency line fusion module (single lines, double lines, and soft edges) for structure guidance, and a two-part painting process (Base Layer Shaping via Retinex-based brightness initialization, and Surface Layer Coloring via texture synthesis and selective attention-layer injection). The paper also introduces the ProLines dataset of 5,101 filtered design drawings and reports quantitative, qualitative, and user-study comparisons against several state-of-the-art methods. The central claim is that LineArt outperforms existing methods in accuracy, realism, and material precision while preserving structural details of the input line drawing.

Significance. Training-free appearance transfer for professional line drawings is practically valuable, and the paper's analogy to classical underpainting/glazing is a creative way to structure the pipeline. The proposed ProLines dataset is a potentially useful resource for the sketch-to-image community, and the use of standard components with no training makes the method easy to adopt if the claims hold. However, the current evidence is not yet convincing: the soft-edge construction is circular, the latent brightness initialization is heuristic, key ablations are deferred to an absent supplementary document, and the quantitative evaluation lacks uncertainty estimates. The paper also does not mention code release or dataset availability in the main text. Strengths include a multi-metric comparison, a user study, and a method description detailed enough to be reimplemented; the weaknesses are concentrated in the validation of the central claims.

major comments (5)
  1. [Section 4.1] The soft-edge construction is circular. The paper computes S_soft = Haar(G_initial), where G_initial is itself generated using ControlNet-depth conditioned on both the line drawing and the appearance image. Therefore Condition_geometry = Fusion(L_double, L_single, S_soft) contains high-frequency information derived from the reference appearance, not only from the original line drawing. This conflates texture with structure and directly threatens the central claim of structure preservation. Please ablate S_soft by replacing it with an edge/high-frequency map computed solely from L_original, and report structure metrics (SSIM/CD) for the variant without S_soft and for the appearance-free soft-edge variant. Without this ablation, the contribution of multi-frequency line fusion to structural fidelity is not established.
  2. [Section 4.2] The Base Layer Shaping initialization x'_0 = Lmean + (x0 - Lmean) * 0.5 lacks a derivation or controlled validation. The paper does not explain why a brightness statistic of the pixel-space Retinex illumination can be additively transplanted into the VAE latent x0, nor what Lmean and x0 are precisely (a scalar, a per-channel vector, or a latent tensor), and the factor 0.5 appears hand-picked. The claim that this creates a 'soft link' between the lighting of the reference and the generated image is not supported by theory or by quantitative ablation; Figure 7(b) is only qualitative. Please clarify the notation, provide a derivation or a rigorous empirical study, and report quantitative ablations over Lmean values and the 0.5 factor to justify this key design choice.
  3. [Table 1 / Section 6.1] The quantitative evaluation reports single values without error bars, statistical significance, or the number of images evaluated per dataset. Several comparisons are close or even reverse: for Bronze, DreamBooth achieves higher PSNR (27.15) than LineArt (25.20), and the FID gap between LineArt and the baseline is small in several rows. The claim that LineArt 'performs better in accuracy, realism, and material precision compared to SOTAs' is therefore not statistically substantiated. Please report mean and standard deviation over multiple seeds or test splits, run significance tests, and specify the evaluation set size and protocol for each metric.
  4. [Section 6.3] The ablation study, which addresses four central design choices (double lines, soft edges, surface-layer coloring, and patch size), is only described as being in the supplementary materials; the main text contains no ablation table or figure, and the arXiv version does not appear to include the supplement. The user study details are also deferred. Since these ablations directly support the method's core contributions, the main text should include at least a compact ablation table and the essential user-study protocol (number of participants, number of stimuli, ranking scale, and statistical analysis).
  5. [Section 5] The ProLines dataset is filtered to hand-chosen IC intervals (e.g., Bronze 0.2576–0.2903, ImageNet-Sketch 0.2500–0.2650) followed by manual screening by the authors. The paper does not state what fraction of images was removed at each stage, whether annotators agreed, or whether the thresholds were selected before or after observing model performance. This creates a risk that the evaluation is conducted on a self-selected subset favorable to the proposed method. Please provide a sensitivity analysis across IC thresholds and report results on a held-out random sample of the unfiltered source data.
minor comments (5)
  1. [Abstract and Section 4.2] There are several typos and grammatical errors: the section title 'Knowleged-guided Structure Preservation' should be 'Knowledge-guided', 'Accoding' should be 'According', and Figure 7's caption contains 'Origianl' instead of 'Original'.
  2. [Section 4.1 and Section 4.2] The notation is inconsistent: 'Conditiongeometry' appears in Section 4.1 while 'Condition_geometry' appears elsewhere; the operators Fusion, TS, and moveBackground are used without precise definitions. Please define all operators and use consistent notation throughout.
  3. [Section 6.1] The metrics GLCM and CH are not defined in the main text. Please specify the exact distance or statistic used for the gray-level co-occurrence matrix and the color histogram loss so that the reported numbers are reproducible.
  4. [Section 5] The 'Bronze' dataset source is not cited in the reference list, and no dataset URL or release statement is provided for ProLines. Please add the missing citation and state how the dataset will be distributed.
  5. [Figures 1 and 8] The list of compared methods is not consistent between the qualitative figures and Table 1: Figure 1 shows T2I-Adapter-SDXL while Table 1 includes DreamBooth and InstructPix2Pix but not T2I-Adapter-SDXL. Please align the comparison methods across all experiments.

Circularity Check

1 steps flagged · score 4.0 of 10

The soft-edge structure channel in Section 4.1 is bootstrapped from a first-pass generation that already uses the appearance reference, making the claimed structure preservation partially self-referential.

  1. self definitional [Section 4.1, Knowledge-guided Structure Preservation (S_soft = Haar(G_initial) and Condition_geometry = Fusion(...))]
    "Next, we use [91] to predict the depth information of the line drawing and generate it for the first time using ControlNet-depth, in combination with the specified appearance image. The initial generation result Ginitial is then used to extract soft edges Ssoft that capture spatial gradients and texture characteristics as Ssoft = Haar(Ginitial). These soft edges act as high-frequency constraints in subsequent appearance generation, ensuring accuracy and naturalness of the results in terms of texture primitives."

    The high-frequency constraints that are fused into Condition_geometry are not derived from the original line drawing L_original. They are extracted from G_initial, a first-pass image produced by ControlNet-depth 'in combination with the specified appearance image.' Thus the same reference appearance that is later injected through IP-Adapter also determines the soft-edge conditioning, so structure and appearance are not cleanly separated. The paper even describes S_soft as capturing 'texture characteristics' and 'texture primitives,' which means the 'geometry' channel partly carries appearance content rather than independent structural evidence from the drawing.

full rationale

LineArt's central claim is high-fidelity appearance transfer with structural accuracy, and most of its pipeline (depth-based ControlNet, L_double/L_single morphology, IP-Adapter material injection, Retinex-based latent mean shift, and the ProLines evaluation against external SOTAs) is independent and empirically testable. The one load-bearing self-reference is the soft-edge term: S_soft = Haar(G_initial), where G_initial is itself generated using the appearance image. This makes the final ControlNet geometry conditioning partly a function of the appearance input, so the method's structural channel is not purely derived from the line drawing. However, the final output is not identical to G_initial, the single- and double-line channels remain tied to L_original, and the quantitative evaluation compares against external baselines, so the circularity is partial rather than total. I found no self-citation chains, uniqueness arguments, or fitted-parameter-as-prediction constructions. The hand-chosen 0.5 factor in x'_0 = Lmean + (x0 - Lmean)*0.5 and the dataset IC thresholds are heuristic choices, not circularities, though they are correctness risks.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The method leans on several unstated assumptions: the line decomposition is cognitively motivated but not verified; the Retinex-to-latent mapping is heuristic; the attention layer choices are borrowed from prior work without an in-paper ablation. The dataset thresholds are hand-selected, and the soft-edge bootstrap introduces a self-referential step.

free parameters (8)
  • Morphological kernel sizes for erode/dilate = not specified
    Used to construct Ldouble and Lsingle in Section 4.1; sizes are not given and affect line emphasis.
  • Retinex multi-scale Gaussian scales and weights = not specified
    Section 4.2 relies on multi-scale Retinex; specific scales and weighting are omitted.
  • Texture synthesis patch size = not specified
    Section 4.2: Tsyn patches the reference image; patch size is deferred to supplementary.
  • Latent brightness adjustment factor = 0.5
    In x'_0 = Lmean + (x0 - Lmean) * 0.5; chosen by hand without sensitivity analysis in main text.
  • IP-Adapter projection feature count = 4
    Section 4.2: image embedding mapped to N=4 feature tokens to match text embedding length.
  • Selected attention layers for material injection = up blocks.0.attentions.1; down blocks.2.attentions.1
    Section 4.2: layers are chosen based on [81], but no ablation is shown in main text.
  • Fusion weights for Conditiongeometry = not specified
    Section 4.1 combines Ldouble, Lsingle, and Ssoft; how they are weighted is not stated.
  • ProLines IC threshold ranges = e.g., Bronze 0.2576-0.2903, DifferSketching 0.0461-0.2165
    Section 5: narrow per-dataset thresholds are hand-selected, which may bias the evaluation sample.
assumptions (6)
  • domain assumption Visual representations of sketches can be decomposed into single lines, double lines, and soft edges (based on Marr's assertions).
    Section 3.1: this decomposition is the foundation of the line fusion module, but is not formally verified.
  • domain assumption The Imprimatura painting technique (underpainting and glazing) maps to two separate diffusion guidance stages.
    Section 3.2: used to justify splitting brightness and texture control.
  • domain assumption Multi-scale Retinex extracts an illumination map that corresponds to perceived brightness.
    Section 4.2: the brightness analysis operates on Lretinex; Retinex assumptions are taken as given.
  • ad hoc to paper The initial latent shift x'_0 = Lmean + (x0 - Lmean) * 0.5 provides a soft brightness link.
    Section 4.2: no theoretical or empirical justification is provided for this mapping.
  • ad hoc to paper The selected attention layers are the right places to inject material features.
    Section 4.2: the choice is motivated by citation [81], not by a dedicated study in this paper.
  • domain assumption Depth Anything's depth estimates on line drawings are reliable enough for ControlNet-depth guidance.
    Section 4.1: the first-pass generation Ginitial depends on this depth prediction.

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Cite this review

Pith. "Pith review of LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model." pith.science (2026). https://pith.science/paper/VAYYLS4W

@misc{pith2026241211519,
  author       = {Pith},
  title        = {Pith review of: LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VAYYLS4W}},
  note         = {Machine review of arXiv:2412.11519}
}
read the original abstract

Image rendering from line drawings is vital in design and image generation technologies reduce costs, yet professional line drawings demand preserving complex details. Text prompts struggle with accuracy, and image translation struggles with consistency and fine-grained control. We present LineArt, a framework that transfers complex appearance onto detailed design drawings, facilitating design and artistic creation. It generates high-fidelity appearance while preserving structural accuracy by simulating hierarchical visual cognition and integrating human artistic experience to guide the diffusion process. LineArt overcomes the limitations of current methods in terms of difficulty in fine-grained control and style degradation in design drawings. It requires no precise 3D modeling, physical property specs, or network training, making it more convenient for design tasks. LineArt consists of two stages: a multi-frequency lines fusion module to supplement the input design drawing with detailed structural information and a two-part painting process for Base Layer Shaping and Surface Layer Coloring. We also present a new design drawing dataset ProLines for evaluation. The experiments show that LineArt performs better in accuracy, realism, and material precision compared to SOTAs.

Figures

Figures reproduced from arXiv: 2412.11519 by the authors.

Figure 1
Figure 1. Generated results by our method and Comparison with SOTAs. (a): Given an object-centered design drawing and a photo as the reference, our method transfers the complex appearance features in the photo to the fine structure of the design drawing with high fidelity. (b): Compared with ZeST [16], StyleID [17], AesPA-Net [37], Cross-Image Attention [1], and T2I-Adapter-SDXL [57]. Our method better ensures the accurate tr… view at source ↗
Figure 2
Figure 2. (a) Various pencils in hardness and color. (b) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. A classical painting technique: Imprimatura. We designate the Underpainting process as Base Layer Shaping and Glazing as Surface Layer Coloring. In the Base Layer Shaping stage, we handle implicit information from the reference image, such as lighting effects, illumination, and shading-based reflectance. Features related to texel and color are addressed in the Surface Layer Coloring stage. sents elongated linear fea… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Our Workflow. The process begins with a design drawing Loriginal and an appearance image Iappearance. Depth-based ControlNet estimates depth and generates soft edges to guide the synthesis. (a) The Multi-frequency Line Fusion module employs assertion-guided techniques …
Figure 5
Figure 5. Figure 5: Moving Background’s Impact on Texture Synthesis. By removing irrelevant background regions, materials embedding become more accurate materials embedding. attribution is obtained by applying erosion to the mask as Lsingle = erode(Mask). Next, we use [91] to predict the …
Figure 6
Figure 6. Figure 6: Construction of ProLines Dataset. (a) shows the types of data we filtered out. (b) shows an overview of the data after the initial screening based on image complexity and manual removal of noise data. (c) shows the data preprocessing of the selected data, including the…
Figure 7
Figure 7. Figure 7: (a.1) Frequency Analysis of Texture Elements in Real Photos. (a.2)Improved material fidelity through [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Our work is compared with other SOTAs in the qualitative results of processing technical drawings with fine structures and images of specified materials. As can be seen from the figure, we can generate more accurate textures while maintaining finer structures (such as …
Figure 9
Figure 9. Figure 9: Examples of our method in assisting design. The upper part shows that for line draft designers, our method can quickly render the technical drawings with the effect of a specified material, helping designers to intuitively evaluate whether the design of the line draft …
Figure 10
Figure 10. Figure 10: Result of User Study. achieves the best performance in most of the metrics. We also conducted a qualitative experiment comparing with seven SOTAs. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]

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Reference graph

Works this paper leans on

98 extracted references · 61 canonical work pages

  1. [1]

    Cross-image attention for zero- shot appearance transfer

    Yuval Alaluf, Daniel Garibi, Or Patashnik, Hadar Averbuch- Elor, and Daniel Cohen-Or. Cross-image attention for zero- shot appearance transfer. In ACM SIGGRAPH 2024 Confer- ence Papers, pages 1–12, 2024. 1, 4, 8

    2024

  2. [2]

    Sketchinr: A first look into sketches as implicit neural representations

    Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Tao Xiang, Timothy Hospedales, and Yi-Zhe Song. Sketchinr: A first look into sketches as implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12565–12574, 2024. 3

    2024

  3. [3]

    What sketch explainability really means for downstream tasks? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 10997–11008, 2024

    Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Tao Xiang, and Yi-Zhe Song. What sketch explainability really means for downstream tasks? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 10997–11008, 2024. 3, 4

    2024

  4. [4]

    Masksketch: Unpaired structure-guided masked image generation

    Dina Bashkirova, Jose Lezama, Kihyuk Sohn, Kate Saenko, and Irfan Essa. Masksketch: Unpaired structure-guided masked image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1879–1889, 2023. 2, 3

    2023

  5. [5]

    Style and abstraction in por- trait sketching

    Itamar Berger, Ariel Shamir, Moshe Mahler, Elizabeth Carter, and Jessica Hodgins. Style and abstraction in por- trait sketching. ACM Transactions on Graphics (TOG) , 32 (4):1–12, 2013. 3

    2013

  6. [6]

    Pixelor: A competitive sketching ai agent

    Ayan Kumar Bhunia, Ayan Das, Umar Riaz Muhammad, Yongxin Yang, Timothy M Hospedales, Tao Xiang, Yulia Gryaditskaya, and Yi-Zhe Song. Pixelor: A competitive sketching ai agent. so you think you can sketch?ACM Trans- actions on Graphics (TOG), 39(6):1–15, 2020. 3

    2020

  7. [7]

    Sketch less for more: On-the-fly fine-grained sketch-based image retrieval

    Ayan Kumar Bhunia, Yongxin Yang, Timothy M Hospedales, Tao Xiang, and Yi-Zhe Song. Sketch less for more: On-the-fly fine-grained sketch-based image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 9779–9788, 2020. 2

    2020

  8. [8]

    B-cos net- works: Alignment is all we need for interpretability

    Moritz B ¨ohle, Mario Fritz, and Bernt Schiele. B-cos net- works: Alignment is all we need for interpretability. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10329–10338, 2022. 4

    2022

Show all 98 references
  1. [9]

    In- structpix2pix: Learning to follow image editing instructions

    Tim Brooks, Aleksander Holynski, and Alexei A Efros. In- structpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer 9 Vision and Pattern Recognition, pages 18392–18402, 2023. 8

    2023

  2. [10]

    Painting materials and working methods.A Corpus of Rembrandt Paintings: 1625–1631 , pages 11–33,

    J Bruyn, B Haak, SH Levie, PJJ Van Thiel, E Van De Weter- ing, and E vd W. Painting materials and working methods.A Corpus of Rembrandt Paintings: 1625–1631 , pages 11–33,

  3. [11]

    Diffusart: Enhancing line art coloriza- tion with conditional diffusion models

    Hernan Carrillo, Micha ¨el Cl ´ement, Aur ´elie Bugeau, and Edgar Simo-Serra. Diffusart: Enhancing line art coloriza- tion with conditional diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3486–3490, 2023. 2

    2023

  4. [12]

    Fast image pro- cessing with fully-convolutional networks

    Qifeng Chen, Jia Xu, and Vladlen Koltun. Fast image pro- cessing with fully-convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision , pages 2497–2506, 2017. 4

    2017

  5. [13]

    Deepfacedrawing: Deep generation of face im- ages from sketches

    Shu Yu Chen, Wanchao Su, Lin Gao, Shihong Xia, and Hongbo Fu. Deepfacedrawing: Deep generation of face im- ages from sketches. ACM Transactions on Graphics, 39(4): 72, 2020. 2, 3

    2020

  6. [14]

    Sketchygan: Towards di- verse and realistic sketch to image synthesis

    Wengling Chen and James Hays. Sketchygan: Towards di- verse and realistic sketch to image synthesis. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 9416–9425, 2018. 2

    2018

  7. [15]

    Adaptively-realistic image gen- eration from stroke and sketch with diffusion model

    Shin-I Cheng, Yu-Jie Chen, Wei-Chen Chiu, Hung-Yu Tseng, and Hsin-Ying Lee. Adaptively-realistic image gen- eration from stroke and sketch with diffusion model. In Pro- ceedings of the IEEE/CVF winter conference on applications of computer vision, pages 4054–4062, 2023. 3

    2023

  8. [16]

    Zest: Zero-shot material trans- fer from a single image

    Ta-Ying Cheng, Prafull Sharma, Andrew Markham, Niki Trigoni, and Varun Jampani. Zest: Zero-shot material trans- fer from a single image. In European Conference on Com- puter Vision, pages 370–386. Springer, 2025. 1, 8

    2025

  9. [17]

    Style injec- tion in diffusion: A training-free approach for adapting large- scale diffusion models for style transfer

    Jiwoo Chung, Sangeek Hyun, and Jae-Pil Heo. Style injec- tion in diffusion: A training-free approach for adapting large- scale diffusion models for style transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8795–8805, 2024. 1, 3, 4, 8

    2024

  10. [18]

    Cross- modal latent space alignment for image to avatar translation

    Manuel Ladron De Guevara, Jose Echevarria, Yijun Li, Yan- nick Hold-Geoffroy, Cameron Smith, and Daichi Ito. Cross- modal latent space alignment for image to avatar translation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 520–529, 2023. 2

    2023

  11. [19]

    Stytr2: Im- age style transfer with transformers

    Yingying Deng, Fan Tang, Weiming Dong, Chongyang Ma, Xingjia Pan, Lei Wang, and Changsheng Xu. Stytr2: Im- age style transfer with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11326–11336, 2022. 2, 3

    2022

  12. [20]

    How do humans sketch objects? ACM Transactions on graphics (TOG) , 31 (4):1–10, 2012

    Mathias Eitz, James Hays, and Marc Alexa. How do humans sketch objects? ACM Transactions on graphics (TOG) , 31 (4):1–10, 2012. 6

    2012

  13. [21]

    Image smoothing via unsupervised learning

    Qingnan Fan, Jiaolong Yang, David Wipf, Baoquan Chen, and Xin Tong. Image smoothing via unsupervised learning. ACM Transactions on Graphics (TOG), 37(6):1–14, 2018. 4

    2018

  14. [22]

    Faragasso

    J. Faragasso. Student’s Guide to Painting . Dover Publica- tions, 2020. 4

    2020

  15. [23]

    Ic9600: a benchmark dataset for automatic image complexity assess- ment

    Tinglei Feng, Yingjie Zhai, Jufeng Yang, Jie Liang, Deng- Ping Fan, Jing Zhang, Ling Shao, and Dacheng Tao. Ic9600: a benchmark dataset for automatic image complexity assess- ment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(7):8577–8593, 2022. 6

    2022

  16. [24]

    Sketchycoco: Image gener- ation from freehand scene sketches

    Chengying Gao, Qi Liu, Qi Xu, Limin Wang, Jianzhuang Liu, and Changqing Zou. Sketchycoco: Image gener- ation from freehand scene sketches. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5174–5183, 2020. 2, 3

    2020

  17. [25]

    Neural image com- pression via attentional multi-scale back projection and fre- quency decomposition

    Ge Gao, Pei You, Rong Pan, Shunyuan Han, Yuanyuan Zhang, Yuchao Dai, and Hojae Lee. Neural image com- pression via attentional multi-scale back projection and fre- quency decomposition. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision , pages 14677–...

    2021

  18. [26]

    Get3d: A generative model of high quality 3d tex- tured shapes learned from images

    Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler. Get3d: A generative model of high quality 3d tex- tured shapes learned from images. In Advances in Neural Information Processing Systems, pages 31841–31854. Cur...

    2022

  19. [27]

    A neural algorithm of artistic style

    Leon A Gatys. A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576, 2015. 3

    2015 arXiv

  20. [28]

    Exploring the structure of a real-time, arbitrary neural artistic stylization network

    Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, and Jonathon Shlens. Exploring the structure of a real-time, arbitrary neural artistic stylization network. arXiv preprint arXiv:1705.06830, 2017. 3

    2017 arXiv

  21. [29]

    Interactive sketch & fill: Multiclass sketch-to-image transla- tion

    Arnab Ghosh, Richard Zhang, Puneet K Dokania, Oliver Wang, Alexei A Efros, Philip HS Torr, and Eli Shechtman. Interactive sketch & fill: Multiclass sketch-to-image transla- tion. In Proceedings of the IEEE/CVF International Confer- ence on Computer Vision, pages 1171–1180, 2019. 3

    2019

  22. [30]

    Opens- ketch: A richly-annotated dataset of product design sketches

    Yulia Gryaditskaya, Mark Sypesteyn, Jan Willem Hoftijzer, Sylvia Pont, Fr ´edo Durand, and Adrien Bousseau. Opens- ketch: A richly-annotated dataset of product design sketches. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) , 38, 2019. 6

    2019

  23. [31]

    Sketch2mesh: Reconstructing and editing 3d shapes from sketches

    Benoit Guillard, Edoardo Remelli, Pierre Yvernay, and Pas- cal Fua. Sketch2mesh: Reconstructing and editing 3d shapes from sketches. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 13023–13032,

  24. [32]

    Cogs: Controllable generation and search from sketch and style

    Cusuh Ham, Gemma Canet Tarres, Tu Bui, James Hays, Zhe Lin, and John Collomosse. Cogs: Controllable generation and search from sketch and style. In European Conference on Computer Vision, pages 632–650. Springer, 2022. 2, 3, 6

    2022

  25. [33]

    Diffusion- enhanced patchmatch: A framework for arbitrary style trans- fer with diffusion models

    Mark Hamazaspyan and Shant Navasardyan. Diffusion- enhanced patchmatch: A framework for arbitrary style trans- fer with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 797–805, 2023. 3

    2023

  26. [34]

    Guided image fil- tering

    Kaiming He, Jian Sun, and Xiaoou Tang. Guided image fil- tering. IEEE transactions on pattern analysis and machine intelligence, 35(6):1397–1409, 2012. 4

    2012

  27. [35]

    Masked autoencoders are scalable vision learners

    Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll´ar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 16000– 16009, 2022. 5 10

    2022

  28. [36]

    Why do line drawings work? a realism hypothesis

    Aaron Hertzmann. Why do line drawings work? a realism hypothesis. Perception, 49(4):439–451, 2020. 2, 3, 4

    2020

  29. [37]

    Aespa-net: Aesthetic pattern-aware style transfer networks

    Kibeom Hong, Seogkyu Jeon, Junsoo Lee, Namhyuk Ahn, Kunhee Kim, Pilhyeon Lee, Daesik Kim, Youngjung Uh, and Hyeran Byun. Aespa-net: Aesthetic pattern-aware style transfer networks. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 22758–22767,

  30. [38]

    Arbitrary style transfer in real-time with adaptive instance normalization

    Xun Huang and Serge Belongie. Arbitrary style transfer in real-time with adaptive instance normalization. In Proceed- ings of the IEEE international conference on computer vi- sion, pages 1501–1510, 2017. 2

    2017

  31. [39]

    Sc-fegan: Face editing gen- erative adversarial network with user’s sketch and color

    Youngjoo Jo and Jongyoul Park. Sc-fegan: Face editing gen- erative adversarial network with user’s sketch and color. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1745–1753, 2019. 2, 3

    2019

  32. [40]

    The quick, draw!-ai experi- ment

    Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jong- min Kim, and Nick Fox-Gieg. The quick, draw!-ai experi- ment. Mount View, CA, accessed Feb, 17(2018):4, 2016. 6

    2018

  33. [41]

    Giampietrino, boltraffio, and the influence of leonardo

    Larry Keith and Ashok Roy. Giampietrino, boltraffio, and the influence of leonardo. National Gallery Technical Bulletin, 17:4–19, 1996. 4

    1996

  34. [42]

    Pic- ture that sketch: Photorealistic image generation from ab- stract sketches

    Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, and Yi-Zhe Song. Pic- ture that sketch: Photorealistic image generation from ab- stract sketches. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 6850–6861,

  35. [43]

    It’s all about your sketch: Democratising sketch control in diffusion models

    Subhadeep Koley, Ayan Kumar Bhunia, Deeptanshu Sekhri, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, and Yi- Zhe Song. It’s all about your sketch: Democratising sketch control in diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog...

    2024

  36. [44]

    Deeppatent: Large scale patent drawing recognition and re- trieval

    Michal Kucer, Diane Oyen, Juan Castorena, and Jian Wu. Deeppatent: Large scale patent drawing recognition and re- trieval. In Proceedings of the IEEE/CVF Winter Confer- ence on Applications of Computer Vision, pages 2309–2318,

  37. [45]

    Photo-sketching: Inferring contour draw- ings from images

    Mengtian Li, Zhe Lin, Radomir Mech, Ersin Yumer, and Deva Ramanan. Photo-sketching: Inferring contour draw- ings from images. In 2019 IEEE Winter Conference on Ap- plications of Computer Vision (WACV) , pages 1403–1412. IEEE, 2019. 4

    2019

  38. [46]

    Gligen: Open-set grounded text-to-image generation

    Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jian- wei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22511–22521, 2023. 2

    2023

  39. [47]

    Visual attribute transfer through deep image analogy

    Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, and Sing Bing Kang. Visual attribute transfer through deep image analogy. arXiv preprint arXiv:1705.01088, 2017. 4

    2017 arXiv

  40. [48]

    Sketchdream: Sketch-based text-to-3d generation and edit- ing

    Feng-Lin Liu, Hongbo Fu, Yu-Kun Lai, and Lin Gao. Sketchdream: Sketch-based text-to-3d generation and edit- ing. ACM Transactions on Graphics (TOG) , 43(4):1–13,

  41. [49]

    Deflocnet: Deep im- age editing via flexible low-level controls

    Hongyu Liu, Ziyu Wan, Wei Huang, Yibing Song, Xintong Han, Jing Liao, Bin Jiang, and Wei Liu. Deflocnet: Deep im- age editing via flexible low-level controls. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10765–10774, 2021. 3

    2021

  42. [50]

    Unsupervised sketch to photo synthesis

    Runtao Liu, Qian Yu, and Stella X Yu. Unsupervised sketch to photo synthesis. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16, pages 36–52. Springer, 2020. 2

    2020

  43. [51]

    Learning re- cursive filters for low-level vision via a hybrid neural net- work

    Sifei Liu, Jinshan Pan, and Ming-Hsuan Yang. Learning re- cursive filters for low-level vision via a hybrid neural net- work. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14 , pages 560–576. Springer,

    2016

  44. [52]

    Image generation from sketch constraint using con- textual gan

    Yongyi Lu, Shangzhe Wu, Yu-Wing Tai, and Chi-Keung Tang. Image generation from sketch constraint using con- textual gan. In Proceedings of the European conference on computer vision (ECCV), pages 205–220, 2018. 3

    2018

  45. [53]

    Early processing of visual information

    David Marr. Early processing of visual information. Philo- sophical Transactions of the Royal Society of London. B, Bi- ological Sciences, 275(942):483–519, 1976. 2, 3

    1976

  46. [54]

    Visual information processing: The structure and creation of visual representations

    David Marr. Visual information processing: The structure and creation of visual representations. Philosophical Trans- actions of the Royal Society of London. B, Biological Sci- ences, 290(1038):199–218, 1980. 2, 3

    1980

  47. [55]

    Sdedit: Guided image synthesis and editing with stochastic differential equa- tions

    Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia- jun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equa- tions. arXiv preprint arXiv:2108.01073, 2021. 3

    2021 arXiv

  48. [56]

    Sked: Sketch-guided text-based 3d editing

    Aryan Mikaeili, Or Perel, Mehdi Safaee, Daniel Cohen-Or, and Ali Mahdavi-Amiri. Sked: Sketch-guided text-based 3d editing. In Proceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 14607–14619, 2023. 2

    2023

  49. [57]

    T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

    Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 4296–4304, 2024. 1, 3

    2024

  50. [58]

    John J. Newman. Glazing and the use of casein procedure methods in a controversial art media. Design, 53(9):217– 217, 1952. 4

    1952

  51. [59]

    Unsupervised learning of visual representations by solving jigsaw puzzles

    Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In Euro- pean conference on computer vision, pages 69–84. Springer,

  52. [60]

    H. Osborne. The Oxford Companion to Art. Clarendon Press,

  53. [61]

    Discourse on the practice of painting and its main processes: Underpainting, overpainting and re- touching

    Jean-Baptiste Oudry. Discourse on the practice of painting and its main processes: Underpainting, overpainting and re- touching. Forelsening holdt ved det franske Acad´emie royale, 2, 1752. 4

  54. [62]

    Zero-shot image-to-image translation

    Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. Zero-shot image-to-image translation. In ACM SIGGRAPH 2023 Conference Proceed- ings, pages 1–11, 2023. 2 11

    2023

  55. [63]

    Dead- iff: An efficient stylization diffusion model with disentan- gled representations

    Tianhao Qi, Shancheng Fang, Yanze Wu, Hongtao Xie, Ji- awei Liu, Lang Chen, Qian He, and Yongdong Zhang. Dead- iff: An efficient stylization diffusion model with disentan- gled representations. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognit...

    2024

  56. [64]

    Sketchxai: A first look at explain- ability for human sketches

    Zhiyu Qu, Yulia Gryaditskaya, Ke Li, Kaiyue Pang, Tao Xi- ang, and Yi-Zhe Song. Sketchxai: A first look at explain- ability for human sketches. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 23327–23337, 2023. 3

    2023

  57. [65]

    Kornia: an open source differentiable computer vision library for pytorch

    Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, and Gary Bradski. Kornia: an open source differentiable computer vision library for pytorch. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3674–3683, 2020. 6

    2020

  58. [66]

    Encoding in style: a stylegan encoder for image-to-image translation

    Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. Encoding in style: a stylegan encoder for image-to-image translation. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 2287–22...

    2021

  59. [67]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 4

    2022

  60. [68]

    Nonlinear total variation based noise removal algorithms

    Leonid I Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4):259–268, 1992. 4

    1992

  61. [69]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 2250...

    2023

  62. [70]

    The sketchy database: learning to retrieve badly drawn bunnies

    Patsorn Sangkloy, Nathan Burnell, Cusuh Ham, and James Hays. The sketchy database: learning to retrieve badly drawn bunnies. ACM Transactions on Graphics (TOG), 35(4):1–12,

  63. [71]

    Grad-cam: Visual explanations from deep networks via gradient-based localization

    Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE in- ternational conference on computer vision , pages 618–626,

  64. [72]

    Alchemist: Parametric control of material proper- ties with diffusion models

    Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, Bill Freeman, and Mark Matthews. Alchemist: Parametric control of material proper- ties with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio...

    2024

  65. [73]

    Learning to sketch with shortcut cycle consistency

    Jifei Song, Kaiyue Pang, Yi-Zhe Song, Tao Xiang, and Tim- othy M Hospedales. Learning to sketch with shortcut cycle consistency. In Proceedings of the IEEE conference on com- puter vision and pattern recognition , pages 801–810, 2018. 6

    2018

  66. [74]

    Bilateral filtering for gray and color images

    Carlo Tomasi and Roberto Manduchi. Bilateral filtering for gray and color images. In Sixth international conference on computer vision (IEEE Cat. No. 98CH36271) , pages 839–

  67. [75]

    Sketch-guided object localization in nat- ural images

    Aditay Tripathi, Rajath R Dani, Anand Mishra, and Anir- ban Chakraborty. Sketch-guided object localization in nat- ural images. In Computer Vision–ECCV 2020: 16th Euro- pean Conference, Glasgow, UK, August 23–28, 2020, Pro- ceedings, Part VI 16, pages 532–547. Springer, 2020. 3

    2020

  68. [76]

    Texture networks: Feed-forward syn- thesis of textures and stylized images

    Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor Lempitsky. Texture networks: Feed-forward syn- thesis of textures and stylized images. arXiv preprint arXiv:1603.03417, 2016. 3

    2016 arXiv

  69. [77]

    Clipasso: Semantically-aware object sketching

    Yael Vinker, Ehsan Pajouheshgar, Jessica Y Bo, Ro- man Christian Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, and Ariel Shamir. Clipasso: Semantically-aware object sketching. ACM Transactions on Graphics (TOG), 41(4):1–11, 2022. 3

    2022

  70. [78]

    Clipascene: Scene sketching with different types and levels of abstraction

    Yael Vinker, Yuval Alaluf, Daniel Cohen-Or, and Ariel Shamir. Clipascene: Scene sketching with different types and levels of abstraction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4146– 4156, 2023. 3

    2023

  71. [79]

    Sketch-guided text-to-image diffusion models

    Andrey V oynov, Kfir Aberman, and Daniel Cohen-Or. Sketch-guided text-to-image diffusion models. In ACM SIG- GRAPH 2023 Conference Proceedings, pages 1–11, 2023. 2, 3

    2023

  72. [80]

    Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019

    Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing. Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019. 6

    2019

  73. [81]

    Instantstyle: Free lunch towards style- preserving in text-to-image generation

    Haofan Wang, Qixun Wang, Xu Bai, Zekui Qin, and Anthony Chen. Instantstyle: Free lunch towards style- preserving in text-to-image generation. arXiv preprint arXiv:2404.02733, 2024. 6

    2024 arXiv

  74. [82]

    Unsupervised scene sketch to photo synthesis

    Jiayun Wang, Sangryul Jeon, Stella X Yu, Xi Zhang, Hi- manshu Arora, and Yu Lou. Unsupervised scene sketch to photo synthesis. In European Conference on Computer Vi- sion, pages 273–289. Springer, 2022. 2

    2022

  75. [83]

    Sketch your own gan

    Sheng-Yu Wang, David Bau, and Jun-Yan Zhu. Sketch your own gan. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14050–14060, 2021. 2, 3

    2021

  76. [84]

    Pretraining is all you need for image-to-image translation

    Tengfei Wang, Ting Zhang, Bo Zhang, Hao Ouyang, Dong Chen, Qifeng Chen, and Fang Wen. Pretraining is all you need for image-to-image translation. arXiv preprint arXiv:2205.12952, 2022. 3

    2022 arXiv

  77. [85]

    Stylediffusion: Controllable disentangled style transfer via diffusion models

    Zhizhong Wang, Lei Zhao, and Wei Xing. Stylediffusion: Controllable disentangled style transfer via diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 7677–7689, 2023. 2, 3

    2023

  78. [86]

    Fine-grained image analysis with deep learning: A survey

    Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, and Serge Belongie. Fine-grained image analysis with deep learning: A survey. IEEE transactions on pattern analysis and machine intelli- gence, 44(12):8927–8948, 2021. 2

    2021

  79. [87]

    Style- former: Real-time arbitrary style transfer via parametric style composition

    Xiaolei Wu, Zhihao Hu, Lu Sheng, and Dong Xu. Style- former: Real-time arbitrary style transfer via parametric style composition. In Proceedings of the IEEE/CVF Interna- 12 tional Conference on Computer Vision, pages 14618–14627,

  80. [88]

    Sketch-based model- ing with a differentiable renderer

    Nan Xiang, Ruibin Wang, Tao Jiang, Li Wang, Yanran Li, Xiaosong Yang, and Jianjun Zhang. Sketch-based model- ing with a differentiable renderer. Computer Animation and Virtual Worlds, 31(4-5):e1939, 2020. 2

    2020

  81. [89]

    Adversarial open domain adap- tation for sketch-to-photo synthesis

    Xiaoyu Xiang, Ding Liu, Xiao Yang, Yiheng Zhu, Xiaohui Shen, and Jan P Allebach. Adversarial open domain adap- tation for sketch-to-photo synthesis. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1434–1444, 2022. 2

    2022

  82. [90]

    Differsketching: How differently do people sketch 3d objects?ACM Transactions on Graphics (TOG), 41(6):1–16, 2022

    Chufeng Xiao, Wanchao Su, Jing Liao, Zhouhui Lian, Yi- Zhe Song, and Hongbo Fu. Differsketching: How differently do people sketch 3d objects?ACM Transactions on Graphics (TOG), 41(6):1–16, 2022. 6

    2022

  83. [91]

    Depth anything: Unleashing the power of large-scale unlabeled data

    Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10371–10381, 2024. 5

    2024

  84. [92]

    Controllable sketch-to-image translation for robust face synthesis

    Shuai Yang, Zhangyang Wang, Jiaying Liu, and Zongming Guo. Controllable sketch-to-image translation for robust face synthesis. IEEE Transactions on Image Processing, 30: 8797–8810, 2021. 2

    2021

  85. [93]

    Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models. arXiv preprint arXiv:2308.06721,

    arXiv

  86. [94]

    Texture- dreamer: Image-guided texture synthesis through geometry- aware diffusion

    Yu-Ying Yeh, Jia-Bin Huang, Changil Kim, Lei Xiao, Thu Nguyen-Phuoc, Numair Khan, Cheng Zhang, Manmohan Chandraker, Carl S Marshall, Zhao Dong, et al. Texture- dreamer: Image-guided texture synthesis through geometry- aware diffusion. In Proceedings of the IEEE/CVF Confer- enc...

    2024

  87. [95]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 2, 3, 4, 8

    2023

  88. [96]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 7

    2018

  89. [97]

    Prospect: Prompt spectrum for attribute-aware personalization of diffusion models.ACM Transactions on Graphics (TOG), 42(6):1–14, 2023

    Yuxin Zhang, Weiming Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Tong-Yee Lee, Oliver Deussen, and Changsheng Xu. Prospect: Prompt spectrum for attribute-aware personalization of diffusion models.ACM Transactions on Graphics (TOG), 42(6):1–14, 2023. 2

    2023

  90. [98]

    Inversion-based style transfer with diffusion models

    Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, and Changsheng Xu. Inversion-based style transfer with diffusion models. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10146–10156, 2023. 3 13

    2023

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.