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arxiv: 2605.10409 · v1 · submitted 2026-05-11 · 💻 cs.CV

Recognition: no theorem link

Progressive Photorealistic Simplification

Adi Rosenthal , Dana Berman , Yedid Hoshen , Ariel Shamir
Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:51 UTC · model grok-4.3

classification 💻 cs.CV
keywords photorealistic simplificationsemantic image editingprogressive removalinpaintingvision-language modelsgenerative editingimage decluttering
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The pith

Images can be simplified photorealistically by iteratively removing objects and inpainting gaps while preserving realism.

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

The paper establishes a method for reducing visual complexity in photographs without turning them into drawings or paintings. It proceeds by using vision-language models to select elements for removal, applying generative inpainting to fill the resulting gaps, and checking each output with a verifier that enforces photorealism and coherence. If the process works, it yields a sequence of increasingly simple yet still natural-looking images from any starting photograph. A sympathetic reader would care because this offers a way to declutter or decompose scenes inside the photographic domain rather than through abstraction.

Core claim

The paper introduces progressive semantic image simplification as an iterative framework that reduces scene complexity through controlled removal and inpainting of elements. At each step the output remains a plausible natural photograph. The method combines VLM-guided selection of what to remove with generative editing and a learned verifier inside a Select-Remove-Verify pipeline. The full process is further distilled into an image-to-video model that predicts coherent simplification sequences directly from a single input image.

What carries the argument

The Select-Remove-Verify pipeline, which uses vision-language models to prioritize removable elements, generative inpainting to restore backgrounds, and a verifier to enforce photorealism after each step.

Load-bearing premise

That VLM selection, inpainting, and verification together can keep every intermediate image free of visible artifacts and scene inconsistencies.

What would settle it

A generated simplification sequence that exhibits obvious mismatches in lighting, shadows, or object boundaries after only a few removal steps.

Figures

Figures reproduced from arXiv: 2605.10409 by Adi Rosenthal, Ariel Shamir, Dana Berman, Yedid Hoshen.

Figure 1
Figure 1. Figure 1: Our method progressively abstracts a photograph by intelligently removing scene elements. Starting with a complex original image (left), our pipeline [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Photorealistic vs. Stylistic Simplification. We compare our method (top rows) against two progressive simplification methods (bottom rows): CLIPascene sketch abstraction [Vinker et al. 2023] and stylistic sim￾plification [Farbman et al. 2008]. The previous approaches simplify scenes by altering the medium (e.g., to sketches or cartoons), our method simplifies semantic content but preserves photographic fid… view at source ↗
Figure 3
Figure 3. Figure 3: Semantic classification of image elements. The figure depicts elements from each class, whose description is written below the image and marked by a bounding box. for the simplification process. We heuristically group the objects in E into four semantic levels of importance (see examples in [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of Classification Agreement. (Left) Row-Normalized Aggregated Confusion Matrix of Human Raters (Inter-rater reliability) demonstrates strong consensus on the “Secondary” class (83.2%), though raters frequently conflate “Background” elements with “Secondary” (32.1%). (Right) Disagreement Analysis of Gemini vs. Human Raters. While Gemini generally aligns with human perception, it exhibits a strong… view at source ↗
Figure 5
Figure 5. Figure 5: Method Overview. Our framework operates in two stages to achieve effective progressive photorealistic simplification. Stage I (Search-Based Trajectory) is an iterative, closed-loop pipeline. A VLM-based Planner identifies scene elements for removal based on semantic rank. The Robust Execution module performs inpainting (Generate, Align, Blend), while a Classifier Edit Verification step ensures the edit pre… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative Comparison. Visual results comparing our Search-Based method (Left) against the Distilled approach (Right). The distilled model maintains temporal consistency while successfully and iteratively removing scene elements [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Interactive Simplification Trees. Visualizing the edit "tree" from Sec. 6. Our interactive planner allows the user to intervene at intermediate states. Here, the user chooses to “branch” the sequence to preserve the wooden table texture, and then removing one object over the other, demon￾strating the system’s flexibility in defining subjective visual hierarchies. can disrupt the scene and may result in a l… view at source ↗
Figure 8
Figure 8. Figure 8: Semantic Image De-cluttering. Our approach automatically iden￾tifies and removes visual noise, such as power lines, transient crowds, and background litter, without requiring manual masks or user prompts. The model produces clean, aesthetic compositions while strictly preserving the primary subject and scene context. “Distracting” and “Secondary” elements in the earliest stages of gen￾eration, the initial … view at source ↗
Figure 9
Figure 9. Figure 9: Semantic Image De-cluttering. Our approach automatically identifies and removes visual noise without requiring manual masks or user prompts. The model produces clean, aesthetically enhanced compositions while strictly preserving the primary subject and scene context [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Automatic Layer Extraction. Our method automatically decom￾poses the input image into independent semantic layers. Based on visual importance, we isolate the scene, enabling object-level editing and scene reconstruction. interactive tree exploration can alleviate that. Moreover, the Stage I removal and inpainting processes rely heavily on the base model, which may occasionally fail or introduce visual art… view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative Comparison. Each block shows (Top) Ours, (Middle) Veo 3.1, and (Bottom) Wan 2.2 [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Classifier Performance. (a) The model achieves 96% True Positive and True Negative rates at the decision threshold of 0.5. (b) Validation accuracy stabilizes after approximately 10 epochs. significantly to only 3.7%. When a preference was expressed in these inter-category pairs, the direction was highly consistent with our hierarchy, as detailed in [PITH_FULL_IMAGE:figures/full_fig_p011_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative Comparison. Each block shows (Top) Ours, (Middle) Veo 3.1, and (Bottom) Wan 2.2 [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: End-to-End Direct Prompting Failure. When state-of-the-art generative models are prompted to generate a progressive abstraction sequence directly, the results are unstable. Each row illustrates a different example where the models fail to perfectly isolate the removals, hallucinate new structures, and progressively lose photorealism across the generated sequence [PITH_FULL_IMAGE:figures/full_fig_p016_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Filtered Bad Edits. Examples of candidate removals generated by the inpainting model that were successfully rejected by our verification classifier. 𝑡0 𝑡10 𝑡15 𝑡20 𝑡25 𝑡30 [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Iterative Prompting Degradation. When performing step-by-step removals using standard image editing, global image quality rapidly diverges [PITH_FULL_IMAGE:figures/full_fig_p017_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: More progressive simplification results [PITH_FULL_IMAGE:figures/full_fig_p018_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: More progressive simplification results [PITH_FULL_IMAGE:figures/full_fig_p019_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: More progressive simplification results [PITH_FULL_IMAGE:figures/full_fig_p020_19.png] view at source ↗
read the original abstract

Existing image simplification techniques often rely on Non-Photorealistic Rendering (NPR), transforming photographs into stylized sketches, cartoons, or paintings. While effective at reducing visual complexity, such approaches typically sacrifice photographic realism. In this work, we explore a complementary direction: simplifying images while preserving their photorealistic appearance. We introduce progressive semantic image simplification, a framework that iteratively reduces scene complexity by removing and inpainting elements in a controlled manner. At each step, the resulting image remains a plausible natural photograph. Our method combines semantic understanding with generative editing, leveraging Vision-Language Models (VLMs) to identify and prioritize elements for removal, and a learned verifier to ensure photorealism and coherence throughout the process. This is implemented via an iterative Select-Remove-Verify pipeline that produces high-quality simplification trajectories. To improve efficiency, we further distill this process into an image-to-video generation model that directly predicts coherent simplification sequences from a single input image. Beyond generating cleaner and more focused compositions, our approach enables applications such as content-aware decluttering, semantic layer decomposition, and interactive editing. More broadly, our work suggests that simplification through structured content removal can serve as a practical mechanism for guiding visual interpretation within the photorealistic domain, complementing traditional abstraction methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces progressive semantic image simplification, a framework that iteratively reduces image complexity via VLM-guided element selection, generative inpainting for removal, and a learned verifier to enforce photorealism and coherence at each step. The process is distilled into an image-to-video model that predicts simplification sequences directly from a single input, enabling applications such as content-aware decluttering and semantic decomposition while preserving photographic realism.

Significance. If validated, the approach would offer a practical photorealistic complement to traditional NPR methods, with potential utility in interactive editing and visual interpretation tasks. The distillation step addresses efficiency, and the iterative Select-Remove-Verify loop is a plausible engineering pipeline. However, the lack of any reported quantitative results, ablations, or multi-step evaluations in the manuscript limits assessment of whether the central guarantee of artifact-free outputs holds.

major comments (3)
  1. [Method description] The learned verifier is described only as 'learned' with no architecture, training corpus, loss functions, thresholds, or multi-step evaluation protocol provided. This is load-bearing for the claim that the Select-Remove-Verify loop reliably prevents accumulated artifacts from generative inpainting across iterations.
  2. [Abstract and evaluation] No quantitative results, ablation studies, failure cases, or metrics (e.g., LPIPS, perceptual consistency, or human ratings on trajectories longer than 2 steps) are reported to support the assertion that each output remains a 'plausible natural photograph.' This undermines verification of the iterative photorealism guarantee.
  3. [Distillation section] The distillation into an image-to-video model is presented as an efficiency improvement, but without details on how the training data is generated from the iterative pipeline or any comparison of quality/consistency between the original loop and the distilled model, the claim that it 'directly predicts coherent simplification sequences' cannot be assessed.
minor comments (2)
  1. [Abstract] The abstract and method overview would benefit from explicit notation for the iterative process (e.g., defining the state after each removal step) to improve clarity for readers.
  2. [Implementation] References to specific VLM and inpainting models used (e.g., versions or fine-tuning details) are missing, which is standard for reproducibility in CV papers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We have revised the manuscript to provide the requested details on the verifier, add quantitative evaluations and ablations, and expand the distillation section with data generation and comparison information.

read point-by-point responses

  1. Referee: [Method description] The learned verifier is described only as 'learned' with no architecture, training corpus, loss functions, thresholds, or multi-step evaluation protocol provided. This is load-bearing for the claim that the Select-Remove-Verify loop reliably prevents accumulated artifacts from generative inpainting across iterations.

    Authors: We agree that the verifier requires more detailed specification to support the iterative photorealism claims. In the revised manuscript, we have added Section 3.3 describing the verifier as a binary classifier operating on CLIP image embeddings, trained on a corpus of 50k synthetic clean/artifacted image pairs generated via controlled inpainting perturbations. Training uses binary cross-entropy loss with an acceptance threshold of 0.75, and we include a multi-step protocol evaluating rejection rates over trajectories of length 5–10. revision: yes

  2. Referee: [Abstract and evaluation] No quantitative results, ablation studies, failure cases, or metrics (e.g., LPIPS, perceptual consistency, or human ratings on trajectories longer than 2 steps) are reported to support the assertion that each output remains a 'plausible natural photograph.' This undermines verification of the iterative photorealism guarantee.

    Authors: We acknowledge that the original submission emphasized the framework over extensive benchmarking. The revised version includes a new Experiments section reporting LPIPS and perceptual consistency scores across simplification trajectories, ablation studies isolating the verifier and VLM selection components, and a human study with ratings on photorealism for trajectories up to 5 steps. Failure cases (e.g., residual artifacts in cluttered scenes) are now discussed with examples in the supplementary material. revision: yes

  3. Referee: [Distillation section] The distillation into an image-to-video model is presented as an efficiency improvement, but without details on how the training data is generated from the iterative pipeline or any comparison of quality/consistency between the original loop and the distilled model, the claim that it 'directly predicts coherent simplification sequences' cannot be assessed.

    Authors: We have expanded the distillation section to explain that training data is generated by executing the full iterative Select-Remove-Verify pipeline on 10k source images to produce input-to-sequence pairs. The revised manuscript now includes direct comparisons: the distilled model retains 90% of the iterative pipeline's human-rated coherence while achieving 15x faster inference, with sequence consistency metrics (e.g., frame-to-frame LPIPS) reported for both approaches. revision: yes

Circularity Check

0 steps flagged

No circularity: engineering pipeline with independent components

full rationale

The paper presents a practical iterative framework (Select-Remove-Verify) relying on external VLMs for element selection, off-the-shelf generative inpainting, and a separately learned verifier. No equations, predictions, or first-principles claims reduce by construction to fitted parameters or self-citations. The central claim of maintaining photorealism is an empirical engineering assertion, not a mathematical derivation that loops back to its inputs. Self-contained against external benchmarks like standard inpainting models and VLM capabilities.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The approach rests on the assumption that existing generative inpainting models can be steered to produce photorealistic outputs after arbitrary object removals; no new physical constants or mathematical axioms are introduced.

invented entities (1)
  • learned verifier no independent evidence
    purpose: to ensure photorealism and coherence after each removal-inpainting step
    The verifier is presented as a new learned component whose training data and architecture are not specified in the abstract.

pith-pipeline@v0.9.0 · 5520 in / 1138 out tokens · 47701 ms · 2026-05-12T03:51:13.708956+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

55 extracted references · 55 canonical work pages · 2 internal anchors

  1. [1]

    ACM Transactions on Graphics (TOG) , volume=

    Real-time edge-aware image processing with the bilateral grid , author=. ACM Transactions on Graphics (TOG) , volume=. 2007 , publisher=

    work page 2007

  2. [2]

    ACM transactions on graphics (TOG) , volume=

    Edge-preserving decompositions for multi-scale tone and detail manipulation , author=. ACM transactions on graphics (TOG) , volume=. 2008 , publisher=

    work page 2008

  3. [3]

    and Siddappa, Nagendraswamy and Manjunath, C

    M P, Pavan Kumar and Poornima, B. and Siddappa, Nagendraswamy and Manjunath, C. , year =. A comprehensive survey on non-photorealistic rendering and benchmark developments for image abstraction and stylization , volume =. Iran Journal of Computer Science , doi =

    work page

  4. [4]

    State of the “Art”: A Taxonomy of Artistic Stylization Techniques for Images and Video , volume =

    Jan Eric Kyprianidis and John Collomosse and Tinghuai Wang and Tobias Isenberg , journal =. State of the “Art”: A Taxonomy of Artistic Stylization Techniques for Images and Video , volume =

    work page

  5. [5]

    ACM transactions on graphics (TOG) , volume=

    Stylization and abstraction of photographs , author=. ACM transactions on graphics (TOG) , volume=. 2002 , publisher=

    work page 2002

  6. [6]

    ACM Transactions On Graphics (TOG) , volume=

    Real-time video abstraction , author=. ACM Transactions On Graphics (TOG) , volume=. 2006 , publisher=

    work page 2006

  7. [7]

    Proceedings of the 25th annual conference on Computer graphics and interactive techniques , pages=

    Painterly rendering with curved brush strokes of multiple sizes , author=. Proceedings of the 25th annual conference on Computer graphics and interactive techniques , pages=

    work page

  8. [8]

    IEEE Transactions on Image Processing , volume=

    Artistic edge and corner enhancing smoothing , author=. IEEE Transactions on Image Processing , volume=. 2007 , publisher=

    work page 2007

  9. [9]

    Computer Graphics Forum , volume=

    Image and video abstraction by anisotropic Kuwahara filtering , author=. Computer Graphics Forum , volume=. 2009 , organization=

    work page 2009

  10. [10]

    Chui , journal=

    Henry Kang and Seungyong Lee and Charles K. Chui , journal=. Flow-Based Image Abstraction , year=

    work page

  11. [11]

    Sander and Adam Finkelstein , title =

    Jingwan Lu and Pedro V. Sander and Adam Finkelstein , title =. Proceedings of ACM SIGGRAPH symposium on Interactive 3D Graphics and Games , year =

    work page

  12. [12]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    Image style transfer using convolutional neural networks , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    work page

  13. [13]

    Proceedings of the 46th Graphics Interface Conference (GI 2020) , pages =

    Rosa Azami and David Mould , title =. Proceedings of the 46th Graphics Interface Conference (GI 2020) , pages =. 2020 , doi =

    work page 2020

  14. [14]

    SIGGRAPH Asia 2015 Technical Briefs , year =

    Shugo Yamaguchi and Takuya Kato and Tsukasa Fukusato and Chie Furusawa and Shigeo Morishima , title =. SIGGRAPH Asia 2015 Technical Briefs , year =. doi:10.1145/2820903.2820917 , publisher =

    work page doi:10.1145/2820903.2820917 2015

  15. [15]

    ACM Transactions on Graphics (TOG) , volume=

    Clipasso: Semantically-aware object sketching , author=. ACM Transactions on Graphics (TOG) , volume=. 2022 , publisher=

    work page 2022

  16. [16]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Clipascene: Scene sketching with different types and levels of abstraction , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    work page

  17. [17]

    IEEE Transactions on image processing , volume=

    Region filling and object removal by exemplar-based image inpainting , author=. IEEE Transactions on image processing , volume=. 2004 , publisher=

    work page 2004

  18. [18]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Segment anything , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

    work page

  19. [19]

    Advances in neural information processing systems , volume=

    Laion-5b: An open large-scale dataset for training next generation image-text models , author=. Advances in neural information processing systems , volume=

    work page

  20. [20]

    , author=

    Lora: Low-rank adaptation of large language models. , author=. ICLR , volume=

    work page

  21. [21]

    Real-Time Video Abstraction , journal =

    Winnem. Real-Time Video Abstraction , journal =

    work page

  22. [22]

    XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization , year =

    Winnem\". XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization , year =. Comput. Graph. , pages =

    work page

  23. [23]

    Computer Graphics Forum , title =

    Li, Simin and Wen, Qiang and Zhao, Shuang and Sun, Zixun and He, Shengfeng , year =. Computer Graphics Forum , title =

    work page

  24. [24]

    IEEE transactions on image processing , volume=

    NIMA: Neural image assessment , author=. IEEE transactions on image processing , volume=. 2018 , publisher=

    work page 2018

  25. [25]

    Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=

    Resolution-robust large mask inpainting with fourier convolutions , author=. Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=

    work page

  26. [26]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Deep saliency prior for reducing visual distraction , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    work page

  27. [27]

    Proceedings of the AAAI Conference on Artificial Intelligence , year=

    Attentive eraser: Removing objects from images with diffusion models , author=. Proceedings of the AAAI Conference on Artificial Intelligence , year=

    work page

  28. [28]

    Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , year=

    OmniEraser: Removing Objects and Their Effects from Images , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , year=

    work page

  29. [29]

    Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , year=

    SimpSON: Simplifying scenes by object neutralization , author=. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , year=

    work page

  30. [30]

    arXiv preprint arXiv:2401.XXXX , year=

    Visual Jenga: Object Removal via Counterfactual Inpainting , author=. arXiv preprint arXiv:2401.XXXX , year=

    work page

  31. [31]

    Advances in Neural Information Processing Systems , volume=

    Diffsketcher: Text guided vector sketch synthesis through latent diffusion models , author=. Advances in Neural Information Processing Systems , volume=

    work page

  32. [32]

    European Conference on Computer Vision , pages=

    Objectdrop: Bootstrapping counterfactuals for photorealistic object removal and insertion , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    work page 2024

  33. [33]

    2025 , url =

    Sharon, David and Brichtova, Nicole , title =. 2025 , url =

    work page 2025

  34. [34]

    2025 , url =

    Google DeepMind , title =. 2025 , url =

    work page 2025

  35. [35]

    Proceedings of the IEEE Conference on Computer Vision and pattern Recognition , pages=

    Finding distractors in images , author=. Proceedings of the IEEE Conference on Computer Vision and pattern Recognition , pages=

    work page

  36. [36]

    2020 , journal =

    Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari , title =. 2020 , journal =

    work page 2020

  37. [37]

    Wan: Open and Advanced Large-Scale Video Generative Models

    Wan: Open and Advanced Large-Scale Video Generative Models , author=. arXiv preprint arXiv:2503.20314 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  38. [38]

    Video models are zero-shot learners and reasoners

    Video models are zero-shot learners and reasoners , author=. arXiv preprint arXiv:2509.20328 , year=

    work page internal anchor Pith review arXiv

  39. [39]

    arXiv preprint arXiv:2511.19435 , year=

    Are Image-to-Video Models Good Zero-Shot Image Editors? , author=. arXiv preprint arXiv:2511.19435 , year=

    work page arXiv

  40. [40]

    Chronoedit: Towards temporal reasoning for image editing and world simulation.arXiv preprint arXiv:2510.04290, 2025

    ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation , author=. arXiv preprint arXiv:2510.04290 , year=

    work page arXiv

  41. [41]

    Biometrika , volume=

    A new measure of rank correlation , author=. Biometrika , volume=. 1938 , publisher=

    work page 1938

  42. [42]

    1948 , publisher=

    Rank Correlation Methods , author=. 1948 , publisher=

    work page 1948

  43. [43]

    arXiv preprint arXiv:2104.03133 , year=

    Image Composition Assessment with Saliency-augmented Multi-pattern Pooling , author=. arXiv preprint arXiv:2104.03133 , year=

    work page arXiv

  44. [44]

    1960 , publisher =

    Art and Illusion: A Study in the Psychology of Pictorial Representation , author =. 1960 , publisher =

    work page 1960

  45. [45]

    2010 , publisher =

    Vision: A Computational Investigation into the Human Representation and Processing of Visual Information , author =. 2010 , publisher =

    work page 2010

  46. [46]

    2008 , publisher =

    Vision and Art: The Biology of Seeing , author =. 2008 , publisher =

    work page 2008

  47. [47]

    1999 , publisher =

    Vision Science: Photons to Phenomenology , author =. 1999 , publisher =

    work page 1999

  48. [48]

    1992 , publisher =

    The Science of Art: Optical Themes in Western Art from Brunelleschi to Seurat , author =. 1992 , publisher =

    work page 1992

  49. [49]

    A source book of Gestalt psychology , pages =

    Wertheimer, Max , title =. A source book of Gestalt psychology , pages =

    work page

  50. [50]

    Proceedings of the 21st annual conference on Computer graphics and interactive techniques , pages=

    Computer-generated pen-and-ink illustration , author=. Proceedings of the 21st annual conference on Computer graphics and interactive techniques , pages=

    work page

  51. [51]

    Proceedings of the 24th annual conference on Computer graphics and interactive techniques , pages=

    Computer-generated watercolor , author=. Proceedings of the 24th annual conference on Computer graphics and interactive techniques , pages=

    work page

  52. [52]

    Proceedings of the 27th annual conference on Computer graphics and interactive techniques , pages=

    Escherization , author=. Proceedings of the 27th annual conference on Computer graphics and interactive techniques , pages=

    work page

  53. [53]

    and Oliver, Nuria and Curless, Brian and Salesin, David H

    Hertzmann, Aaron and Jacobs, Charles E. and Oliver, Nuria and Curless, Brian and Salesin, David H. , title =. Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques , pages =. 2001 , isbn =. doi:10.1145/383259.383295 , abstract =

    work page doi:10.1145/383259.383295 2001

  54. [54]

    Axiomathes , volume=

    Defining pictorial style: Lessons from linguistics and computer graphics , author=. Axiomathes , volume=. 2005 , publisher=

    work page 2005

  55. [55]

    An invitation to discuss computer depiction , year =

    Durand, Fr\'. An invitation to discuss computer depiction , year =. Proceedings of the 2nd International Symposium on Non-Photorealistic Animation and Rendering , pages =. doi:10.1145/508530.508550 , abstract =

    work page doi:10.1145/508530.508550