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REVIEW 4 major objections 5 minor 1 cited by

PreciseCam: Precise Camera Control for Text-to-Image Generation

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Four camera parameters, encoded as per-pixel maps, give text-to-image models precise camera control.

desk verdict Clean conditioning recipe and a useful dataset, but the paper never measures the camera accuracy it claims, so 'precise' remains an assertion. read the letter →

arxiv 2501.12910 v1 pith:G3XG7B6V submitted 2025-01-22 cs.CV cs.AIcs.LG

Edurne Bernal-Berdun , Ana Serrano , Belen Masia , Matheus Gadelha , Yannick Hold-Geoffroy , Xin Sun , Diego Gutierrez This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords cameracontroltext-to-imagegenerationdiffusionmodelsNetPerspectiveFieldUnifiedSphericalmodelpanoramadatasetlensdistortion
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

The paper claims that text-to-image diffusion models can be given precise camera control by conditioning them on a per-pixel geometric map derived from just four user-set parameters: roll, pitch, vertical field of view, and lens distortion. The authors build this map, called a PF-US map, by combining the Perspective Field representation with the Unified Spherical camera model, and train a ControlNet adapter for SDXL on a new dataset of 57,380 images cropped from 360-degree panoramas with ground-truth camera parameters. If the claim holds, artists and designers could specify camera language numerically, such as a Dutch angle, a worm's-eye view, or a fisheye lens, instead of coaxing the model through prompt engineering, while keeping the content fully described by the text prompt. The paper also shows the conditioning surviving artistic styles, noise variation, and prompt variation, plus applications to object-compositing backgrounds and frame-by-frame video camera control.

What carries the argument

The PF-US map is a two-channel image in which each pixel stores the up-vector (opposed to gravity) and the latitude angle of its viewing ray, computed through the Unified Spherical camera projection function $P(X)$, whose distortion parameter $\xi$ interpolates from pinhole to omnidirectional optics. These maps convert the four abstract camera parameters into local per-pixel appearance cues, so a ControlNet can learn the mapping without any 3D scene representation or multi-view data. At inference, only the ControlNet middle-block residuals are injected into SDXL, which the paper finds gives the best trade-off between camera adherence and image quality.

What would settle it

Generate a set of images with known parameter quartets, then run an off-the-shelf single-image camera calibrator (for example, a Perspective-Field estimator) on each output and plot estimated versus requested roll, pitch, vertical field of view, and $\xi$; if the estimates do not track the requested values, or the error is no better than prompt-engineered SDXL, the precision claim is falsified.

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Extended reading notes

Core claim

PreciseCam's central claim is that four camera parameters, roll, pitch, vertical field of view, and distortion $\xi$, fully determine the geometric appearance of a generated image once they are encoded, pixel by pixel, as up-vectors and latitude angles (the Perspective Field) under the Unified Spherical camera model. A ControlNet trained on these PF-US maps can steer Stable Diffusion XL to produce images whose camera geometry matches the specified settings, while prompt adherence, measured by CLIP and BLIP scores, stays comparable to the base model. The paper demonstrates systematic variation of each parameter with other settings fixed, showing consistent adherence across photographic and artistic outputs, and argues this exceeds what prompt engineering or preset style tags can achieve.

Load-bearing premise

The premise is that patches cropped from 360-degree panoramas, with their corresponding PF-US maps, are representative training material for what a real or artistic image looks like under a given camera setting, so that a model trained on them will reproduce the geometry in generated images; the paper never measures camera accuracy on the outputs.

Editorial extensions

If this is right

  • A user can specify roll, pitch, vertical field of view, and $\xi$ with sliders and get generated images that visually follow those settings, with content decided by the text prompt.
  • The same camera conditioning holds across different noise seeds and small prompt variations, meaning the control is independent of content.
  • Artistic styles such as paintings, ink wash, and lithograph follow the camera conditioning even though the training crops come from photographic panoramas.
  • The method can generate object-matched backgrounds for compositing and can condition each frame of a video, or anchor the initial frame for relative camera-control video models.
  • PreciseCam composes with other ControlNets, so pose, depth, or edge control can be combined with camera control in one generation.

Reading between the lines

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

  • Because the conditioning is a purely 2D geometric map, the same PF-US interface could be plugged into other UNet diffusion backbones, or applied to image editing and inpainting, without retraining the base model; this transfer is plausible but not tested in the paper.
  • The paper's precision claim is qualitative; a natural extension is a quantitative benchmark that estimates roll, pitch, vertical field of view, and $\xi$ from generated images and reports error against the requested values, which would reveal which parameter ranges are actually reliable.
  • Yaw is deliberately excluded because a single image has no left-right reference; a testable consequence is that conditioning with an explicit scene reference, such as a text-specified landmark direction, might enable full six-degree-of-freedom control from the same map representation.
  • The panorama-cropping dataset construction could be reused to generate camera-labeled data for other conditioning tasks, such as depth or normal estimation under extreme fields of view, where real paired data is scarce.
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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

4 major / 5 minor

Summary. PreciseCam introduces a ControlNet-based conditioning module for SDXL that takes four camera parameters — roll, pitch, vertical field of view (vFoV), and distortion ξ — and encodes them as Perspective Field maps under the Unified Spherical camera model (PF-US). The maps are computed deterministically from the parameters via Eq. (3) and fed into a ControlNet. The authors contribute a dataset of 57,380 images with ground-truth camera parameters, obtained by sampling and cropping 360° panoramas, with BLIP-2 generated prompts. The paper presents qualitative results varying each parameter, comparisons with prompt-engineered SDXL and Adobe Firefly, CLIP/BLIP prompt-adherence scores, an ablation of residual injection locations, and proof-of-concept applications to background generation, video control, and multi-ControlNet setups. The central claim is that PreciseCam provides precise, continuous camera control while preserving prompt adherence.

Significance. The contribution is timely and the formulation is clean: using PF-US as a lightweight camera conditioning representation avoids 3D reconstruction and multi-view data. The dataset is a valuable resource, and the code and model are promised publicly. The ablation identifying bottleneck-only residual injection is a useful practical finding. However, the headline claim of 'precise' camera control rests entirely on qualitative image inspection and prompt-adherence metrics; no measurement establishes that generated images match the specified camera parameters. If the authors add a quantitative camera-accuracy evaluation, the paper would be strong.

major comments (4)
  1. [Sec. 4, Figs. 5–8] The central claim of 'precise camera control' is not supported by any quantitative measurement of camera geometry. Figures 5–7 are qualitative, and the CLIP/BLIP scores in Fig. 8 measure prompt adherence only; they do not test whether a requested roll=30° actually produces a 30° roll, whether vFoV=20° vs 50° changes the field of view by the specified amount, or whether ξ=0.1 vs 0.9 yields the correct distortion profile. Because the PF-US map is a deterministic function of the four parameters, the training signal is self-consistent, but the model could learn a qualitative approximation of each parameter without achieving the numeric value, which is exactly what 'precise' claims. Please add a quantitative camera-accuracy metric, for example by running a single-image calibrator such as Jin et al. [24] or GeoCalib [41] on generated images, or by generating images from 3D scenes with known camera parameters and measuring the error.
  2. [Sec. 4, Comparisons and Fig. 7] The comparison to prompt-engineered SDXL and Adobe Firefly does not quantify camera-control accuracy. Fig. 8 reports only CLIP and BLIP scores, which establish that prompt adherence is preserved but cannot distinguish whether the desired camera view was achieved. To support the Abstract claim that PreciseCam 'surpasses traditional prompt engineering approaches,' the paper should report a camera-parameter error metric or a forced-choice human study across methods, so that camera-control success is measured directly.
  3. [Sec. 3.1, Eq. (3); Sec. 3.3] The conversion from (roll, pitch, vFoV, ξ) to PF-US is not fully specified. Eq. (3) uses focal length f and distortion ξ, but the paper never states how the vertical field of view is converted into f, nor what image height is assumed, nor how ξ modifies that relationship in the Unified Spherical model. Without this formula, the dataset generation and the conditioning maps are not reproducible, and the mapping from user-facing camera parameters to the learning signal is ambiguous. Please provide the explicit vFoV-to-f relation for the US model, including the image height used.
  4. [Sec. 5 and Sec. 3.3] The dataset-transfer premise — that crops from 360° panoramas teach the model to produce geometrically correct views for arbitrary prompts, including artistic content largely absent from training — is plausible but unverified, and the limitations in Sec. 5 show that precision is not uniform (extreme roll, prompt-camera conflicts). The requested quantitative evaluation should be stratified by prompt style (photographic vs artistic) and by parameter range, so that the reader can see where 'precise' holds and where the acknowledged failure modes emerge.
minor comments (5)
  1. [Supplementary Sec. B] In the parameter sampling description, 'two ξ values (low ∈ [15, 60) and high ∈ [60, 140))' appears to be a typo: these ranges are in degrees and describe vFoV, while ξ lies in (0,1). Please correct.
  2. [Fig. 5 caption] The caption states 'Top row: Variation in pitch' but Sec. 4 and the row labels indicate that the top row varies roll; please align the caption with the figure and text.
  3. [Sec. 1] The sentence 'Our model, PreciseCam, archives precise camera view control' should read 'achieves.'
  4. [Sec. 5] The phrase 'perfectioning novel tools' should be 'perfecting.'
  5. [Sec. 4.1] The 'Camera conditioning adherence' subsection refers the reader to the supplementary material for examples but provides no quantitative measure of stability across noise; adding a small statistic, such as variation of estimated camera parameters across noise seeds, would strengthen this claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the camera-parameter-to-PF-US mapping is a deterministic projection model, training uses ground-truth crops, and the visual evaluation, though weak, is not a self-referential reduction.

full rationale

The claimed derivation is self-contained: user-provided camera parameters (roll, pitch, vFoV, xi) are converted into PF-US conditioning maps by the closed-form Unified Spherical projection (Eq. 3) plus Perspective Field definitions (Eqs. 1-2). The ControlNet is trained on 57,380 crops of 360-degree panoramas whose ground-truth PF-US maps are computed from the same deterministic equations, so the conditioning signal is not fitted to the target outputs. The main weakness is that the central claim of 'precise' camera control is supported only by qualitative image series (Figs. 5-7) and prompt-adherence scores (Fig. 8), with no quantitative measurement that generated images actually realize the specified roll, pitch, vFoV, or xi; this is an evaluation/validation gap, not a circularity. The adoption of Perspective Fields [24] is prior external work (published at CVPR 2023) and is used as a representation rather than as a self-justifying uniqueness argument; one co-author overlap does not make the cited representation circular. No fitted parameter is renamed as a prediction, no self-citation is load-bearing for the core result, and no known result is merely renamed. Score 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the sufficiency of the PF-US representation as a camera-control condition and on the transferability of a model trained on synthetic panorama crops. No new physical entities or fitted constants are introduced; the camera parameters are user inputs, and the model weights are learned via supervised training.

assumptions (4)
  • standard math The Unified Spherical camera model defines the projection P(X) for central cameras, and PF maps can be computed from it.
    Used in Sec. 3.1 to convert the four camera parameters into per-pixel up-vectors and latitude values. This is established in prior work [5,8].
  • domain assumption A 2D PF-US map encodes sufficient information about the camera view for a diffusion model to infer the correct image appearance.
    The authors assert that PF-US captures the essential per-pixel effects without 3D geometry (Sec. 3.1). This is the core representational premise; if false, the conditioning is insufficient.
  • domain assumption Crops rendered from 360-degree panoramas under the US model provide ground-truth training data that transfers to real photographs and artistic images.
    The dataset is generated by sampling camera parameters and cropping 360-degree images (Sec. 3.3, Supp. B). The generalization to natural and painted images is assumed and only qualitatively demonstrated.
  • domain assumption ControlNet residuals injected only at the bottleneck achieve camera adherence without semantic distortion.
    Justified via an ablation study (Sec. 4.1, Fig. 9) and reference [6]. This design choice is empirical, not derived.

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Cite this review

Pith. "Pith review of PreciseCam: Precise Camera Control for Text-to-Image Generation." pith.science (2026). https://pith.science/paper/G3XG7B6V

@misc{pith2026250112910,
  author       = {Pith},
  title        = {Pith review of: PreciseCam: Precise Camera Control for Text-to-Image Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G3XG7B6V}},
  note         = {Machine review of arXiv:2501.12910}
}
read the original abstract

Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is missing in current text-to-image models. We propose an efficient and general solution that allows precise control over the camera when generating both photographic and artistic images. Unlike prior methods that rely on predefined shots, we rely solely on four simple extrinsic and intrinsic camera parameters, removing the need for pre-existing geometry, reference 3D objects, and multi-view data. We also present a novel dataset with more than 57,000 images, along with their text prompts and ground-truth camera parameters. Our evaluation shows precise camera control in text-to-image generation, surpassing traditional prompt engineering approaches. Our data, model, and code are publicly available at https://graphics.unizar.es/projects/PreciseCam2024.

Figures

Figures reproduced from arXiv: 2501.12910 by the authors.

Figure 1
Figure 1. Our approach enhances the artistic expression of text-to-image generative models by incorporating precise control over camera [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Red boxes: Current approaches rely on trial-and-error prompt engineering or generalistic tags, offering limited camera control in text-to-image generative AI. Others use 3D representations of the scenes, from which depth or edge maps are obtained, but this imposes strict constraints on the resulting image, limiting flexibility. Green box: Our method relies on two extrinsic and two intrinsic camera parameters, user-p… view at source ↗
Figure 3
Figure 3. Left and center: PF-US camera view representation. PF-US camera parameters (roll, pitch, vertical FoV, and distortion ξ) and associated maps corresponding to two example images. PF-US maps encode, for each pixel, a latitude value φx (blue-red color map with contour lines) and a unit up-vector ux (green arrows). Right: Dataset generation. We generate training data in the form of images with ground-truth PF-US maps by… view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Training of our proposed approach for precise camera [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Extrinsic Parameter Control. Images generated by varying the extrinsic camera parameters while keeping the rest of the conditioning fixed, showing consistent adherence to the camera specification. Top row: Variation in pitch, effectively shifting the view from looking …
Figure 6
Figure 6. Figure 6: Intrinsic Parameter Control. Images generated by varying the intrinsic camera parameters while keeping the rest of the conditioning fixed, showing consistent adherence to the camera specification. Left: Variation in the vertical field of view (vFoV); fixed parameters a…
Figure 7
Figure 7. Figure 7: Comparison of our model with SDXL and Adobe Firefly (Firefly tags are in dark blue background; prompt engineering, when [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Our model achieves prompt adherence on par with [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 11
Figure 11. Figure 11: Video generation results showing two examples of [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]
Figure 10
Figure 10. Figure 10: Background generation without and with camera con [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 12
Figure 12. Figure 12: Limitations. Left: The model may on some occasions incorrectly position objects or people in a vertical, standing orien￾tation (like the man on the boat), as it is more frequent. Right: In the presence of conflicts between the prompt and the desired cam￾era view, the …
Figure 13
Figure 13. Figure 13: Incorrect PF map estimations using the model from Jin [PITH_FULL_IMAGE:figures/full_fig_p010_13.png]
Figure 14
Figure 14. Figure 14: Generated images for different input noises but using the same prompt and camera parameters. [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]
Figure 15
Figure 15. Figure 15: Generated images for small variations in prompt using the same noise and camera parameters. [PITH_FULL_IMAGE:figures/full_fig_p012_15.png]
Figure 16
Figure 16. Figure 16: PreciseCam is compatible with previous ControlNets, including pose control (left) and depth control (right). We show￾case control over the person’s pose while simultaneously control￾ling the camera view, and our ability to generate images based on depth inputs while m…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance

    cs.CV 2025-05 conditional novelty 6.0 of 10

    EPiC trains a 30M-parameter visibility-aware ControlNet on mask-based anchor videos from 5,000 in-the-wild videos and 500 steps, reaching SOTA camera accuracy on RealEstate10K and MiraData.

Reference graph

Works this paper leans on

53 extracted references · 41 canonical work pages · cited by 1 Pith paper

  1. [24]

    Perspective fields for single image camera calibration

    Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Blackburn-Matzen, Matthew Sticha, and David F Fouhey. Perspective fields for single image camera calibration. In CVPR, 2023. 2, 3, 4, 1

    work page 2023

  2. [41]

    GeoCalib: Single-image Cali- bration with Geometric Optimization

    Alexander Veicht, Paul-Edouard Sarlin, Philipp Linden- berger, and Marc Pollefeys. GeoCalib: Single-image Cali- bration with Geometric Optimization. In ECCV, 2024. 1

    work page 2024

  3. [1]

    360cities dataset

    360cities. 360cities dataset. https://www.360cities.net/. 5, 1

    work page

  4. [2]

    Adobe. Firefly. https://www.adobe.com/products/firefly.html. 2

    work page

  5. [3]

    ediff-i: Text-to-image dif- fusion models with an ensemble of expert denoisers

    Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Ji- aming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, et al. ediff-i: Text-to-image dif- fusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022. 2

    arXiv 2022

  6. [4]

    Multidiffusion: Fusing diffusion paths for controlled image generation

    Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. 2023. 2

    2023

  7. [5]

    A unifying geometric representation for cen- tral projection systems

    Joao P Barreto. A unifying geometric representation for cen- tral projection systems. Computer Vision and Image Under- standing, 2006. 2, 4

    work page 2006

  8. [6]

    Loosec- ontrol: Lifting controlnet for generalized depth conditioning

    Shariq Farooq Bhat, Niloy Mitra, and Peter Wonka. Loosec- ontrol: Lifting controlnet for generalized depth conditioning. In ACM SIGGRAPH 2024 Conference Papers , pages 1–11,

    work page 2024

Show all 53 references
  1. [7]

    Stable video diffusion: Scaling latent video diffusion models to large datasets

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 8

    2023 arXiv

  2. [8]

    Deepcalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cam- eras

    Oleksandr Bogdan, Viktor Eckstein, Francois Rameau, and Jean-Charles Bazin. Deepcalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cam- eras. In Proceedings of the 15th ACM SIGGRAPH European Conference on Visual Media Production, 2018. 4

    2018

  3. [9]

    Photographic image syn- thesis with cascaded refinement networks

    Qifeng Chen and Vladlen Koltun. Photographic image syn- thesis with cascaded refinement networks. In ICCV, pages 1511–1520, 2017. 2

    2017

  4. [10]

    Sketch2photo: Internet image montage

    Tao Chen, Ming-Ming Cheng, Ping Tan, Ariel Shamir, and Shi-Min Hu. Sketch2photo: Internet image montage. ACM SIGGRAPH, 2009. 2

    2009

  5. [11]

    Learning continuous 3D words for text-to- image generation

    Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, and Niki Trigoni. Learning continuous 3D words for text-to- image generation. In CVPR, 2024. 2, 3

    2024

  6. [12]

    Zero-shot spatial layout conditioning for text-to-image diffusion models

    Guillaume Couairon, Marlene Careil, Matthieu Cord, St´ephane Lathuiliere, and Jakob Verbeek. Zero-shot spatial layout conditioning for text-to-image diffusion models. In ICCV, 2023. 2

    2023

  7. [13]

    Efros and William T

    Alexei A. Efros and William T. Freeman. Image quilting for texture synthesis and transfer. ACM SIGGRAPH, 2001. 2

    2001

  8. [14]

    Efros and Thomas K

    Alexei A. Efros and Thomas K. Leung. Texture synthesis by non-parametric sampling. In ICCV, 1999. 2

    1999

  9. [15]

    Photosketch: A sketch based image query and compositing system

    Mathias Eitz, Kristian Hildebrand, Tamy Boubekeur, and Marc Alexa. Photosketch: A sketch based image query and compositing system. In ACM SIGGRAPH, 2009. 2

    2009

  10. [16]

    Diffusion self-guidance for control- lable image generation

    Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. Diffusion self-guidance for control- lable image generation. NeurIPS, 2023. 2

    2023

  11. [17]

    Accelerate: Training and inference at scale made simple, efficient and adaptable

    Sylvain Gugger, Lysandre Debut, Thomas Wolf, Philipp Schmid, Zachary Mueller, Sourab Mangrulkar, Marc Sun, and Benjamin Bossan. Accelerate: Training and inference at scale made simple, efficient and adaptable. https: //github.com/huggingface/accelerate , 2022. 1

    2022

  12. [18]

    HDRIs dataset

    Poly Haven. HDRIs dataset. https://polyhaven.com/hdris. 5, 1

  13. [19]

    Scene completion using millions of photographs

    James Hays and Alexei A Efros. Scene completion using millions of photographs. ACM SIGGRAPH, 2007. 2

    2007

  14. [20]

    Cameractrl: Enabling camera control for text-to-video generation

    Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrl: Enabling camera control for text-to-video generation. arXiv preprint arXiv:2404.02101, 2024. 3, 8

    2024 arXiv

  15. [21]

    Image analogies

    Aaron Hertzmann, Charles E Jacobs, Nuria Oliver, Brian Curless, and David H Salesin. Image analogies. ACM SIG- GRAPH, 2001. 2

    2001

  16. [22]

    Clipscore: A reference-free evaluation met- ric for image captioning

    Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation met- ric for image captioning. arXiv preprint arXiv:2104.08718,

    arXiv

  17. [23]

    Image-to-image translation with conditional adver- sarial networks

    Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adver- sarial networks. In CVPR, 2017. 2

    2017

  18. [25]

    Semantic photo synthesis

    Matthew Johnson, Gabriel J Brostow, Jamie Shotton, Ognjen Arandjelovic, Vivek Kwatra, and Roberto Cipolla. Semantic photo synthesis. In Comput. Graph. Forum, 2006. 2

    2006

  19. [26]

    Kingma and Jimmy Ba

    Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, 2014. 1

    2014

  20. [27]

    Customizing text-to-image diffusion with camera viewpoint control

    Nupur Kumari, Grace Su, Richard Zhang, Taesung Park, Eli Shechtman, and Jun-Yan Zhu. Customizing text-to-image diffusion with camera viewpoint control. arXiv preprint arXiv:2404.12333, 2024. 2, 3

    2024 arXiv

  21. [28]

    Distortion-adaptive salient object detection in 360º omnidi- rectional images

    Jia Li, Jinming Su, Changqun Xia, and Yonghong Tian. Distortion-adaptive salient object detection in 360º omnidi- rectional images. IEEE Journal of Selected Topics in Signal Processing, 2019. 5, 1

    2019

  22. [29]

    Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In In- ternational conference on machine learning, 2023. 5, 2

    2023

  23. [30]

    Ctrl- adapter: An efficient and versatile framework for adapt- ing diverse controls to any diffusion model

    Han Lin, Jaemin Cho, Abhay Zala, and Mohit Bansal. Ctrl- adapter: An efficient and versatile framework for adapt- ing diverse controls to any diffusion model. arXiv preprint arXiv:2404.09967, 2024. 8

    2024 arXiv

  24. [31]

    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. In ICLR, 2022. 2

    2022

  25. [32]

    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 AAAI, 2024. 3

    2024

  26. [33]

    Semantic segmentation of outdoor panoramic images

    Semih Orhan and Yalin Bastanlar. Semantic segmentation of outdoor panoramic images. Signal, Image and Video Pro- cessing, 2022. 5, 1 4

    2022

  27. [34]

    Karran Pandey, Paul Guerrero, Metheus Gadelha, Yannick Hold-Geoffroy, Karan Singh, and Niloy J. Mitra. Diffusion handles: Enabling 3d edits for diffusion models by lifting activations to 3d. CVPR, 2024. 2

    2024

  28. [35]

    Semantic image synthesis with spatially-adaptive nor- malization

    Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. Semantic image synthesis with spatially-adaptive nor- malization. In CVPR, 2019. 2

    2019

  29. [36]

    Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 4

    2023 arXiv

  30. [37]

    Kandinsky: an improved text-to-image syn- thesis with image prior and latent diffusion, 2023

    Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Malt- seva, Vladimir Arkhipkin, Igor Pavlov, Ilya Ryabov, An- gelina Kuts, Alexander Panchenko, Andrey Kuznetsov, and Denis Dimitrov. Kandinsky: an improved text-to-image syn- thesis with image prior and latent diffusion, 2023. 2

    2023

  31. [38]

    Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer

    Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. CVPR, 2021

    2021

  32. [39]

    Photorealistic text-to-image diffusion models with deep language understanding

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. NeurIPS, 2022. 2

    2022

  33. [40]

    Saliency in VR: How do people explore virtual envi- ronments? IEEE TVCG, 2018

    Vincent Sitzmann, Ana Serrano, Amy Pavel, Maneesh Agrawala, Diego Gutierrez, Belen Masia, and Gordon Wet- zstein. Saliency in VR: How do people explore virtual envi- ronments? IEEE TVCG, 2018. 5, 1

    2018

  34. [42]

    Video-to- video synthesis

    Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. Video-to- video synthesis. arXiv preprint arXiv:1808.06601, 2018. 2

    2018 arXiv

  35. [43]

    High-resolution image syn- thesis and semantic manipulation with conditional gans

    Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. High-resolution image syn- thesis and semantic manipulation with conditional gans. In CVPR, 2018. 2

    2018

  36. [44]

    Videocomposer: Compositional video synthesis with motion controllability

    Xiang Wang, Hangjie Yuan, Shiwei Zhang, Dayou Chen, Ji- uniu Wang, Yingya Zhang, Yujun Shen, Deli Zhao, and Jin- gren Zhou. Videocomposer: Compositional video synthesis with motion controllability. NeurIPS, 2024. 3, 8

    2024

  37. [45]

    Motionctrl: A unified and flexible motion controller for video generation

    Zhouxia Wang, Ziyang Yuan, Xintao Wang, Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In ACM SIGGRAPH, 2024

    2024

  38. [46]

    Camco: Camera- controllable 3d-consistent image-to-video generation

    Dejia Xu, Weili Nie, Chao Liu, Sifei Liu, Jan Kautz, Zhangyang Wang, and Arash Vahdat. Camco: Camera- controllable 3d-consistent image-to-video generation. arXiv preprint arXiv:2406.02509, 2024

    2024 arXiv

  39. [47]

    Dragnuwa: Fine-grained control in video generation by integrating text, image, and trajectory

    Shengming Yin, Chenfei Wu, Jian Liang, Jie Shi, Houqiang Li, Gong Ming, and Nan Duan. Dragnuwa: Fine-grained control in video generation by integrating text, image, and trajectory. arXiv preprint arXiv:2308.08089, 2023. 3, 8

    2023 arXiv

  40. [48]

    Scaling autoregressive models for content-rich text-to-image generation

    Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gun- jan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yin- fei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu. Scaling autoregressive models for content...

    2022

  41. [49]

    Scenecomposer: Any-level semantic image synthesis

    Yu Zeng, Zhe Lin, Jianming Zhang, Qing Liu, John Collo- mosse, Jason Kuen, and Vishal M Patel. Scenecomposer: Any-level semantic image synthesis. In CVPR, 2023. 2

    2023

  42. [50]

    Stack- gan: Text to photo-realistic image synthesis with stacked generative adversarial networks

    Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiao- gang Wang, Xiaolei Huang, and Dimitris N Metaxas. Stack- gan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In ICCV, 2017. 2

    2017

  43. [51]

    Stack- gan++: Realistic image synthesis with stacked generative ad- versarial networks

    Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiao- gang Wang, Xiaolei Huang, and Dimitris N Metaxas. Stack- gan++: Realistic image synthesis with stacked generative ad- versarial networks. IEEE TPAMI, 2018. 2

    2018

  44. [52]

    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 CVPR, 2023. 2, 3, 4, 8, 1

    2023

  45. [53]

    A fixation-based 360 benchmark dataset for salient object detection

    Yi Zhang, Lu Zhang, Wassim Hamidouche, and Olivier De- forges. A fixation-based 360 benchmark dataset for salient object detection. In ICIP, 2020. 5, 1 5

    2020

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.