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REVIEW 3 major objections 6 minor 1 cited by

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Depth-conditioned diffusion texturing of synthetic scenes performs no better than random textures.

desk verdict A careful, honest negative result on diffusion texturing for synthetic keypoint and segmentation data, but the parity claim is underdetermined by single-seed runs and unmeasured annotation noise. read the letter →

arxiv 2411.10164 v1 pith:6INLC2EH submitted 2024-11-15 cs.RO

Thomas Lips , Francis wyffels This is my paper · ORCID
classification cs.RO
keywords syntheticdatagenerationdomainrandomizationtext-to-imagediffusionkeypointdetectionsemanticsegmentationsim-to-realControlNetroboticmanipulation
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

This paper asks whether pretrained text-to-image diffusion models can replace hand-set random textures in synthetic data pipelines for robotic manipulation. The authors generate images of shoes, T-shirts, and mugs by texturing 3D scenes either with random colors and backgrounds or with a depth-conditioned diffusion model, then train keypoint-detection and segmentation networks on both kinds of data. Evaluating on real-world photographs, they find the diffusion-textured data performs on par with random textures, despite looking more realistic. The practical claim is that, for object-centric keypoints and masks, diffusion texturing does not currently reduce the sim-to-real gap or the engineering effort.

What carries the argument

The load-bearing apparatus is a depth-conditioned text-to-image synthesis pipeline: a ControlNet that takes a rendered depth image of the scene plus a text prompt and outputs an RGB texture. The depth conditioning is meant to keep the object's geometry and pose fixed, preserving the pixel-perfect annotations computed from the 3D scene. The paper also experiments with two- and three-stage variants, different prompt sources (fixed captions, BLIP captions, LLM-generated descriptions), and the ControlNet conditioning scale.

What would settle it

A direct measurement of annotation corruption: take synthetic scenes, generate diffusion textures, then re-project the known 3D keypoints onto the generated RGB images and compare human or automated keypoint localization in the image. If annotation error rates are substantial (e.g., more than 5–10% of keypoints displaced by more than a few pixels), the parity with random textures would be fully explained by noisy labels, and the conclusion that diffusion texturing itself provides no benefit would be undercut.

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Extended reading notes

Core claim

The central discovery is a null result: on three object categories, synthetic images textured by a depth-conditioned text-to-image diffusion model yield downstream keypoint and segmentation performance on par with images textured with random materials, while both methods often beat a real-data baseline. The paper reports that diffusion texturing helps on mugs (17.4 vs 18.3 AKD) and T-shirt segmentation (0.99 vs 0.97 mAP) but hurts on shoes (19.6 vs 13.4 AKD) and T-shirt keypoints (45.8 vs 37.9 AKD), leading to the overall conclusion that apparent realism does not automatically transfer into training utility.

Load-bearing premise

The core assumption is that depth conditioning keeps the generated textures aligned with the original 3D scene well enough that the pre-computed keypoint and mask annotations remain correct; if the model bends shapes or shifts object appearance, training labels become noisy.

Editorial extensions

If this is right

  • If this null result holds, visual realism alone is not a reliable proxy for synthetic-data quality; training utility must be measured downstream.
  • Diffusion texturing adds complexity and compute without consistent gain over random textures for keypoint and segmentation tasks, so practitioners may prefer the simpler random pipeline.
  • The performance plateau at roughly 5,000 images for both methods suggests neither approach fully closes the sim-to-real gap for these tasks.
  • Prompt diversity matters: LLM-generated prompts outperform fixed class-name captions, and BLIP captions sit in between, so better prompts are not enough to overturn the overall parity.
  • The depth-conditioned pipeline requires roughly the same per-image compute as random texturing with a physically-based renderer, so speed does not favor diffusion texturing either.

Reading between the lines

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

  • The parity could be explained by annotation pollution: if the diffusion model subtly alters object semantics (e.g., mug handle shape), parts of the training set carry wrong keypoint labels, which would cancel the benefit of realism. The paper itself raises this hypothesis but does not measure it directly; a direct test would compare predicted keypoints on regenerated images with the original surfa
  • The result may not generalize to tasks where exact geometry matters less, such as image classification or open-vocabulary detection, where diffusion texturing could show clearer gains. The paper's chosen tasks are precisely those where geometry preservation is critical.
  • The 'for now' conclusion is time-dependent: as diffusion models improve in geometric consistency, the balance could tilt, and future comparisons should include a direct annotation-corruption metric to separate label noise from texture quality.
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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

3 major / 6 minor

Summary. The paper evaluates whether depth-conditioned Stable Diffusion (via ControlNet) can serve as a texturing method for synthetic data generation for keypoint detection and instance segmentation in robotic manipulation. For three object categories (shoes, T-shirts, mugs), the authors generate synthetic scenes with predetermined pixel-perfect annotations, texture them either with random textures or with a diffusion model, train downstream models on the resulting data, and evaluate on real-world test sets. The central claim is that diffusion-based texturing performs on par with random textures, despite producing seemingly more realistic images, and therefore does not currently benefit synthetic data generation for robotics. The paper also compares prompting strategies, studies data scaling, explores two- and three-stage diffusion pipelines, ablates image conditioning, and examines the ControlNet conditioning scale. Code, data, and trained models are released.

Significance. If the central claim holds, the paper is a useful negative result for the robotics synthetic-data community: photorealistic texturing via large pretrained diffusion models may not automatically reduce the sim-to-real gap for precise pixel-level tasks. The study is well scoped, uses a controlled setup with 3D-scene-derived annotations, evaluates two task families and multiple design choices, and the authors are candid about limitations in the Discussion. The release of code, data, and models is a strength. However, the validity of the negative conclusion depends on assumptions about annotation fidelity and on whether the reported parity is statistically meaningful; both need additional support before the conclusion can be accepted.

major comments (3)
  1. [§III-B.2 and §V] The cornerstone of the comparison is that the predetermined pixel-perfect annotations remain valid after texturing. Section III-B.2 asserts that depth conditioning 'make[s] sure that this texturing does not alter the semantics of the object, ensuring the accuracy of the predetermined scene annotations,' but Section V concedes that the diffusion network has a 'tendency to slightly alter the object semantics (e.g., change the shape of the mug handle slightly), polluting the annotations.' These statements are in direct tension, and the manuscript never measures the frequency or magnitude of this annotation pollution. If a nontrivial fraction of diffusion-textured images carry shifted keypoints or masks, models trained on them learn from partly wrong labels, which could depress downstream performance exactly as observed in Table II. The parity result therefore cannot distinguish 'diffusion texturing provides no benefit' from 'diffusion texturing provides a benefit that is canceled by annotation noise.' Please quantify annotation fidelity (for example, by measuring keypoint displacement between the projected 3D annotations and the generated RGB content, or by re-annotating a sample of diffusion-textured images) and re-state the conclusion accordingly.
  2. [Tables II–IV and Fig. 2] All downstream results are single-seed point estimates with no variance, confidence intervals, or statistical testing. The cross-category direction of the differences is inconsistent: diffusion is better on mug AKD (17.4 vs. 18.3) but worse on shoe AKD (19.6 vs. 13.4) and T-shirt AKD (45.8 vs. 37.9). Calling this 'on par' is a visual reading of numbers that may well be within training noise. Please report multiple training seeds for each pipeline, add error bars, and apply a paired significance test or an equivalence-style analysis before claiming parity. Figure 2 likewise shows scaling curves without error bars; the 'starts to plateau around 5,000 images' reading should be supported by quantitative curve analysis or shaded uncertainty bands.
  3. [§IV-D.5 and Fig. 4] The ControlNet conditioning scale (CCS) is selected by comparing keypoint AKD values across categories, which appears to use the same real evaluation set and the same metric as the headline comparison in Table II. The text does not say how many CCS values were tried, whether a validation split was used, or how the chosen default of 1.5 was justified beyond 'a sensible default.' If the diffusion column in Table II is the result of evaluation-set selection, the comparison is not a clean point estimate. Please document the selection protocol and, if possible, re-run the comparison with a held-out validation split or show that the conclusion is robust across a range of CCS values.
minor comments (6)
  1. [Table II caption] The caption states 'Both outperform the real baseline,' but the T-shirt AKD row shows real=25.6 vs. random=37.9 and diffusion=45.8, which contradicts the caption; the caption should be corrected to 'in most cases' or made cell-specific.
  2. [Abstract and §IV-D.2] The abstract contains a typo: 'dificult' should be 'difficult,' and Section IV-D.2 uses 'dataset slits' where 'dataset splits' is meant.
  3. [References] References [22] and [30] are the same paper (Rombach et al.) and should be merged; reference [11] contains a typo, 'Robotics Sience and Systems.'
  4. [Fig. 2] Figure 2 would benefit from labeled axes, error bands, and a clear marker for the claimed plateau at 5,000 images; currently the plateau is asserted only in the text.
  5. [Fig. 4] The note that CCS=1.5 is 'marked in green' may be invisible in grayscale printing; using a distinct marker symbol in addition to color would make the default value unambiguous.
  6. [§IV-D.3] The three-stage approach is described as being able to 'completely eliminate' the object-extension issue, but Table IV shows that it still underperforms the one-stage approach on most metrics; please soften this claim or provide direct evidence that the issue is absent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central comparison is an independent empirical evaluation on held-out real data, and the acknowledged annotation-pollution confound is a limitation, not a definitional equivalence.

full rationale

The paper's central claim—that depth-conditioned diffusion texturing performs on par with random textures for training keypoint and segmentation models—is established by training models on each synthetic pipeline and measuring AKD/mAP on separately collected real-world test sets (Section IV-B, Table II). No fitted parameter is renamed as a prediction: the ControlNet conditioning scale is chosen on the basis of the reported keypoint experiments (Section IV-D5), but the final comparison is not forced by construction, and the parity result holds across categories and tasks with mixed outcomes. The authors' prior work [10] supplies T-shirt meshes, the aRTF evaluation dataset, and the keypoint protocol, but none of these citations carries the load of the diffusion-vs-random conclusion; the held-out real benchmarks are external to the present comparison. The Discussion's admission that diffusion texturing can 'slightly alter the object semantics... polluting the annotations' (Section V) identifies a genuine confound that could explain the observed parity, but the conclusion is not equivalent to its inputs by definition; it is an empirical result with an acknowledged threat to validity. Accordingly, no circular step can be exhibited from the text.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

No new entities are postulated. The only numbers the conclusions depend on are the conditioning scale and dataset size, both chosen from the paper's own measurements and acknowledged by the authors.

free parameters (2)
  • ControlNet conditioning scale = 1.5
    Selected from Fig. 4 based on keypoint AKD across all three categories; the paper notes category-specific optima differ, so the parity conclusion is conditional on this shared value.
  • Synthetic dataset size per category = 5000
    Chosen from the scaling experiment (Fig. 2) where performance plateaus around 5000 images; used for all reported comparisons.
assumptions (3)
  • domain assumption Depth-conditioned ControlNet texturing preserves object semantics so that projected pixel-perfect annotations remain valid
    Invoked in Section III-B.2 to justify that diffusion texturing does not invalidate predetermined annotations; the Discussion itself questions this with observed mug-handle shape changes.
  • domain assumption The three object categories and the authors' real-world test sets are representative for robotic manipulation keypoint and segmentation transfer
    Used in Sections IV-A and IV-C; generalizing the parity result beyond mugs, shoes, and T-shirts assumes these categories are informative.
  • domain assumption Behavior at 5000 images approximates asymptotic behavior for both texturing methods
    Based on the plateau in Fig. 2; the paper uses this assumption to set dataset size and draw the parity conclusion.

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Cite this review

Pith. "Pith review of Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data." pith.science (2026). https://pith.science/paper/6INLC2EH

@misc{pith2026241110164,
  author       = {Pith},
  title        = {Pith review of: Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6INLC2EH}},
  note         = {Machine review of arXiv:2411.10164}
}
read the original abstract

Building generic robotic manipulation systems often requires large amounts of real-world data, which can be dificult to collect. Synthetic data generation offers a promising alternative, but limiting the sim-to-real gap requires significant engineering efforts. To reduce this engineering effort, we investigate the use of pretrained text-to-image diffusion models for texturing synthetic images and compare this approach with using random textures, a common domain randomization technique in synthetic data generation. We focus on generating object-centric representations, such as keypoints and segmentation masks, which are important for robotic manipulation and require precise annotations. We evaluate the efficacy of the texturing methods by training models on the synthetic data and measuring their performance on real-world datasets for three object categories: shoes, T-shirts, and mugs. Surprisingly, we find that texturing using a diffusion model performs on par with random textures, despite generating seemingly more realistic images. Our results suggest that, for now, using diffusion models for texturing does not benefit synthetic data generation for robotics. The code, data and trained models are available at \url{https://github.com/tlpss/diffusing-synthetic-data.git}.

Figures

Figures reproduced from arXiv: 2411.10164 by the authors.

Figure 1
Figure 1. Left: In this work, we compare text-to-image diffusion models against random textures for texturing 3D scenes in a synthetic data generation pipeline. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Illustration of the one-stage approach issue: the background and object [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 2
Figure 2. Scaling behavior of the different texturing approaches. For both [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison of average keypoint distances for different values of [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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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

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    cs.CV 2025-08 conditional novelty 5.0 of 10

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Reference graph

Works this paper leans on

41 extracted references · 20 canonical work pages · cited by 1 Pith paper

  1. [1]

    kpam: Keypoint af- fordances for category-level robotic manipulation,

    L. Manuelli, W. Gao, P. Florence, and R. Tedrake, “kpam: Keypoint af- fordances for category-level robotic manipulation,” in The International Symposium of Robotics Research . Springer, 2019, pp. 132–157

    work page 2019

  2. [2]

    Learning hand-eye coordination for robotic grasping with deep learning and large- scale data collection,

    S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large- scale data collection,” The International journal of robotics research , vol. 37, no. 4-5, pp. 421–436, 2018

    work page 2018

  3. [3]

    Interactive language: Talking to robots in real time,

    C. Lynch, A. Wahid, J. Tompson, T. Ding, J. Betker, R. Baruch, T. Armstrong, and P. Florence, “Interactive language: Talking to robots in real time,” IEEE Robotics and Automation Letters , 2023

    2023

  4. [4]

    Real-world robot applications of foundation models: a review,

    K. Kawaharazuka, T. Matsushima, A. Gambardella, J. Guo, C. Paxton, and A. Zeng, “Real-world robot applications of foundation models: a review,” Advanced Robotics , vol. 38, no. 18, pp. 1232–1254, 2024

    work page 2024

  5. [5]

    A review of synthetic image data and its use in computer vision,

    K. Man and J. Chahl, “A review of synthetic image data and its use in computer vision,” Journal of Imaging , vol. 8, no. 11, 2022

    2022

  6. [6]

    Photorealistic image synthesis for object instance detection,

    T. Hoda ˇn, V . Vineet, R. Gal, E. Shalev, J. Hanzelka, T. Connell, P. Urbina, S. N. Sinha, and B. Guenter, “Photorealistic image synthesis for object instance detection,” in 2019 IEEE international conference on image processing (ICIP) . IEEE, 2019, pp. 66–70

    2019

  7. [7]

    Fake it till you make it: face analysis in the wild using synthetic data alone,

    E. Wood, T. Baltru ˇsaitis, C. Hewitt, S. Dziadzio, T. J. Cashman, and J. Shotton, “Fake it till you make it: face analysis in the wild using synthetic data alone,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 3681–3691

    work page 2021

  8. [8]

    replicant: a pipeline for generating annotated images of animals in complex environments using unreal engine,

    F. Plum, R. Bulla, H. K. Beck, N. Imirzian, and D. Labonte, “replicant: a pipeline for generating annotated images of animals in complex environments using unreal engine,” Nature Communications , vol. 14, no. 1, p. 7195, 2023

    work page 2023

Show all 41 references
  1. [9]

    Learning dexterous in-hand manipulation,

    O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray et al., “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research, vol. 39, no. 1, pp. 3–20, 2020

    2020

  2. [10]

    Learning keypoints for robotic cloth manipulation using synthetic data,

    T. Lips, V .-L. De Gusseme, and F. wyffels, “Learning keypoints for robotic cloth manipulation using synthetic data,” IEEE Robotics and Automation Letters, vol. 9, no. 7, pp. 6528–6535, 2024

    2024

  3. [11]

    Scaling robot learning with seman- tically imagined experience,

    T. Yu, T. Xiao, A. Stone, J. Tompson, A. Brohan, S. Wang, J. Singh, C. Tan, J. Peralta, B. Ichter et al. , “Scaling robot learning with seman- tically imagined experience,” in Robotics Sience and Systems , 2023

    2023

  4. [12]

    Genaug: Retargeting behaviors to unseen situations via generative augmentation,

    Z. Chen, S. Kiami, A. Gupta, and V . Kumar, “Genaug: Retargeting behaviors to unseen situations via generative augmentation,” in Pro- ceedings of the Robotics: Science and Systems Conference , 2023

    2023

  5. [13]

    Diffusionseg: Adapting diffusion towards unsupervised object discovery,

    C. Ma, Y . Yang, C. Ju, F. Zhang, J. Liu, Y . Wang, Y . Zhang, and Y . Wang, “Diffusionseg: Adapting diffusion towards unsupervised object discovery,” arXiv preprint arXiv:2303.09813 , 2023

    2023 arXiv

  6. [14]

    Adding conditional control to text-to-image diffusion models,

    L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3836–3847

    2023

  7. [15]

    Generating images with 3d annotations using diffusion models,

    W. Ma, Q. Liu, J. Wang, A. Wang, X. Yuan, Y . Zhang, Z. Xiao, G. Zhang, B. Lu, R. Duan, Y . Qi, A. Kortylewski, Y . Liu, and A. Yuille, “Generating images with 3d annotations using diffusion models,” 2024. [Online]. Available: https://arxiv.org/abs/2306.08103

    2024 arXiv

  8. [16]

    Training deep networks with synthetic data: Bridging the reality gap by domain randomization,

    J. Tremblay, A. Prakash, D. Acuna, M. Brophy, V . Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: Bridging the reality gap by domain randomization,” in Proceedings of the IEEE conference on computer vision and...

    2018

  9. [17]

    Retinagan: An object-aware approach to sim-to-real transfer,

    D. Ho, K. Rao, Z. Xu, E. Jang, M. Khansari, and Y . Bai, “Retinagan: An object-aware approach to sim-to-real transfer,” in 2021 IEEE Inter- national Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 10 920–10 926

    2021

  10. [18]

    This dataset does not exist: training models from generated images,

    V . Besnier, H. Jain, A. Bursuc, M. Cord, and P. P´erez, “This dataset does not exist: training models from generated images,” in ICASSP 2020- 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 1–5

    2020

  11. [19]

    Generative adversarial networks,

    I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020

    2020

  12. [20]

    Datasetgan: Efficient labeled data factory with minimal human effort,

    Y . Zhang, H. Ling, J. Gao, K. Yin, J.-F. Lafleche, A. Barriuso, A. Tor- ralba, and S. Fidler, “Datasetgan: Efficient labeled data factory with minimal human effort,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 145–10 155

    2021

  13. [21]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840– 6851, 2020

    2020

  14. [23]

    Effective data augmentation with diffusion models,

    B. Trabucco, K. Doherty, M. Gurinas, and R. Salakhutdinov, “Effective data augmentation with diffusion models,” in The Twelfth International Conference on Learning Representations , 2024

    2024

  15. [24]

    Scaling laws of synthetic images for model training... for now,

    L. Fan, K. Chen, D. Krishnan, D. Katabi, P. Isola, and Y . Tian, “Scaling laws of synthetic images for model training... for now,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 7382–7392

    2024

  16. [25]

    The unmet promise of synthetic training images: Using retrieved real images performs better,

    S. Geng, C.-Y . Hsieh, V . Ramanujan, M. Wallingford, C.-L. Li, P. W. Koh, and R. Krishna, “The unmet promise of synthetic training images: Using retrieved real images performs better,” 2024. [Online]. Available: https://arxiv.org/abs/2406.05184

    2024 arXiv

  17. [26]

    Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic seg- mentation,

    Q. Nguyen, T. Vu, A. Tran, and K. Nguyen, “Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic seg- mentation,” Advances in Neural Information Processing Systems, vol. 36, 2024

    2024

  18. [27]

    Diffumask: Syn- thesizing images with pixel-level annotations for semantic segmentation using diffusion models,

    W. Wu, Y . Zhao, M. Z. Shou, H. Zhou, and C. Shen, “Diffumask: Syn- thesizing images with pixel-level annotations for semantic segmentation using diffusion models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 1206–1217

    2023

  19. [28]

    Medical diffusion on a budget: textual inversion for medical image generation,

    B. De Wilde, A. Saha, R. P. ten Broek, and H. Huisman, “Medical diffusion on a budget: textual inversion for medical image generation,” arXiv preprint arXiv:2303.13430 , 2023

    2023 arXiv

  20. [29]

    An image is worth one word: Personalizing text-to- image generation using textual inversion,

    R. Gal, Y . Alaluf, Y . Atzmon, O. Patashnik, A. H. Bermano, G. Chechik, and D. Cohen-Or, “An image is worth one word: Personalizing text-to- image generation using textual inversion,” in The Eleventh International Conference on Learning Representations , 2023

    2023

  21. [30]

    High- resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High- resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695

    2022

  22. [31]

    Objaverse: A universe of annotated 3d objects,

    M. Deitke, D. Schwenk, J. Salvador, L. Weihs, O. Michel, E. VanderBilt, L. Schmidt, K. Ehsani, A. Kembhavi, and A. Farhadi, “Objaverse: A universe of annotated 3d objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 142–13 153

    2023

  23. [32]

    Google scanned objects: A high- quality dataset of 3d scanned household items,

    L. Downs, A. Francis, N. Koenig, B. Kinman, R. Hickman, K. Reymann, T. B. McHugh, and V . Vanhoucke, “Google scanned objects: A high- quality dataset of 3d scanned household items,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2553–2560

    2022

  24. [33]

    Blender - a 3d modelling and rendering package,

    Blender Online Community, “Blender - a 3d modelling and rendering package,” http://www.blender.org, Blender Foundation

  25. [34]

    Diffusers: State-of-the-art diffusion models

    P. von Platen, S. Patil, A. Lozhkov, P. Cuenca, N. Lambert, K. Rasul, M. Davaadorj, D. Nair, S. Paul, S. Liu, W. Berman, Y . Xu, and T. Wolf, “Diffusers: State-of-the-art diffusion models.” [Online]. Available: https://github.com/huggingface/diffusers

  26. [35]

    S3k: Self-supervised semantic keypoints for robotic manipulation via multi-view consistency,

    M. Vecerik, J.-B. Regli, O. Sushkov, D. Barker, R. Pevceviciute, T. Roth¨orl, R. Hadsell, L. Agapito, and J. Scholz, “S3k: Self-supervised semantic keypoints for robotic manipulation via multi-view consistency,” in Conference on Robot Learning . PMLR, 2021, pp. 449–460

    2021

  27. [36]

    Objects as points,

    X. Zhou, D. Wang, and P. Kr ¨ahenb¨uhl, “Objects as points,” arXiv preprint arXiv:1904.07850, 2019

    1904 arXiv

  28. [37]

    U-net: Convolutional networks for biomedical image segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international con- ference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 ...

    2015

  29. [38]

    Maxvit: Multi-axis vision transformer,

    Z. Tu, H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, and Y . Li, “Maxvit: Multi-axis vision transformer,” in European conference on computer vision . Springer, 2022, pp. 459–479

    2022

  30. [39]

    Ultralytics yolov8,

    G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics yolov8,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics

    2023

  31. [40]

    Microsoft coco: Common objects in context,

    T.-Y . Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll ´ar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 . Springer,...

    2014

  32. [41]

    Clipscore: A reference-free evaluation metric for image captioning,

    J. Hessel, A. Holtzman, M. Forbes, R. L. Bras, and Y . Choi, “Clipscore: A reference-free evaluation metric for image captioning,” arXiv preprint arXiv:2104.08718, 2021

    2021 arXiv

  33. [42]

    Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

    J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International conference on machine learning . PMLR, 2022, pp. 12 888–12 900

    2022

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.