Pith. sign in

REVIEW 3 major objections 5 minor 81 references

BAG: Body-Aligned 3D Wearable Asset Generation

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read BAG generates 3D wearable assets already aligned to a target body, so they can be dressed automatically without manual intervention.

desk verdict BAG is a genuinely new body-aligned 3D wearable asset pipeline with a clever conditioning signal, but its quantitative evaluation and untested pose generalization keep it from being fully convincing. read the letter →

arxiv 2501.16177 v1 pith:NQBOMG47 submitted 2025-01-27 cs.CV cs.AIcs.GR

Zhongjin Luo , Yang Li , Mingrui Zhang , Senbo Wang , Han Yan , Xibin Song , Taizhang Shang , Wei Mao
show 3 more authors
Hongdong Li Xiaoguang Han Pan Ji
This is my paper · ORCID
classification cs.CVcs.AIcs.GR
keywords 3Dwearableassetgenerationbody-alignedmultiviewimagediffusionControlNetconditioningSim(3)optimizationphysics-basedpenetrationresolutiongarmentmodelingsingle-viewreconstruction
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 tries to establish that 3D wearable assets—garments, shoes, and headwear—can be generated directly in alignment with a given 3D human body, so they can be dressed automatically with no manual dragging, morphing, or template deformation. The proposed method, BAG, controls a single-image-to-multiview diffusion model with body surface maps, feeds the resulting body-aligned views into a native 3D diffusion model, and then refines the fit with silhouette-supervised similarity-transformation optimization and physics-based penetration resolution. The authors report that this beats existing garment and wearable-asset methods on image-prompt following, shape diversity, and shape quality. If correct, the work turns an interactive dressing step into a mostly automatic generation step.

What carries the argument

The load-bearing mechanism is the body-conditioned multiview image generator: an auxiliary control network inserted into the consistent multiview diffusion model, conditioned on four-view maps whose pixel values encode the XYZ coordinates of the target body surface in a canonical space. Those maps tell the diffusion model where the body surface is in each orthographic view; since the training assets were retargeted to bodies sampled in shape and pose, the network learns to emit asset views that line up with the body projections. This bridge matters because it converts the body constraint into the same image domain the multiview-to-3D model consumes, so any native 3D diffusion model can be used downstream without changing its weights. The later stages—optimizing a scale-rotation-translation transform in the similarity group against silhouettes and running a position-based dynamics simulation on a proxy mesh—carry the residual alignment and penetration correction.

What would settle it

Run the pipeline on bodies in seated, crouching, or arms-raised poses estimated from real photos and measure the overlap between each generated multiview asset silhouette and the corresponding body projection; if the overlap drops sharply compared with standard standing poses, the body-conditioned generator has not generalized and the downstream steps cannot recover a misaligned asset.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that body alignment can be achieved at the image-generation stage rather than as a post-hoc deformation: by conditioning a consistent multiview diffusion model on four orthographic projections of the target body whose pixels encode canonical 3D surface coordinates, the generated multiview images themselves are already aligned to the body. A native 3D diffusion model then turns those four views into a watertight shape, and because the alignment signal enters before 3D generation, the output asset inherits the body's pose and proportions instead of requiring the user to warp a generic template. The remaining similarity-transformation optimization and physics solver are treated as corrections for the diffusion model's nondeterminism and for local intersections, not as the source of alignment.

Load-bearing premise

The load-bearing premise is that a body-conditioned multiview generator trained on standard standing poses and ordinary body shapes will also align assets to bodies in arbitrary poses and unusual shapes estimated from a single real image; if that transfer fails, none of the later alignment steps can repair a fundamentally misaligned geometry.

Editorial extensions

If this is right

  • Wearable asset creation for avatars shifts from interactive dressing to a feed-forward pipeline: input image and body in, dressed-ready asset out.
  • The same aligned multiview images can drive different native 3D diffusion backbones, so improvements in those backbones transfer directly to wearable-asset quality.
  • Garments, shoes, and headwear are generated in a shared canonical body space, making reposing, automatic fitting, and assembling multiple asset components onto one body feasible without template deformation.
  • The reported shape-quality and alignment gains position the method as a strong baseline for single-image garment and wearable reconstruction, including in virtual try-on settings.

Reading between the lines

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

  • A corollary the paper does not spell out is that the same conditioning recipe applies to any articulated object with a canonical surface coordinate space, so the method generalizes beyond human bodies to animals or robots.
  • Because the training poses are only mild variations of standard standing poses, a natural extension is to test and then include extreme poses in the conditioning data, since the current training distribution does not cover them.
  • The alignment step leans on clean 2D silhouettes, so a practical improvement would be to supplement silhouette supervision with depth or normal cues when the asset is partially occluded in the input image.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes BAG, a pipeline for generating 3D wearable assets (garments, shoes, headwear) aligned to a target SMPLX body from a single image. The pipeline consists of: (1) a Zero123++-style multiview diffusion model with a ControlNet conditioned on XYZ-coordinate body maps, trained on a new dataset of 70,485 body-aligned asset-body pairs with poses interpolated between T-pose and A-pose; (2) a native 3D diffusion model (CLAY/Tripo) that consumes the multiview images; (3) Sim(3) optimization with silhouette supervision; and (4) XPBD-based penetration resolution using a single-layer proxy mesh. The method is compared quantitatively and qualitatively against five baselines and includes ablation studies. The central claim is that BAG produces body-aligned assets that can be automatically dressed on a target body, with significant advantages in prompt-following, diversity, and shape quality.

Significance. If the claimed results hold, BAG is a useful contribution to automated garment and wearable-asset generation: it combines a general multiview generator with a body-conditioned ControlNet and a post-alignment stage, and the dataset of 70,485 asset-body pairs is a potential resource for future work. The paper provides quantitative comparisons on shape metrics (CD/NC/P2S) and multiview metrics (PSNR/SSIM/LPIPS), and ablations that isolate the contribution of body conditioning and of the Sim(3)+penetration alignment. The main weakness is that the evaluation does not test generalization to the arbitrary body poses that the primary input route (Sec. 3.4a) is intended to handle, and the quantitative evaluation lacks error bars, sample sizes, and baseline-adaptation details. The method is not mathematically circular, and the held-out test split is independent in a statistical sense, but it is drawn from the same T/A/Betas distribution as the training data.

major comments (3)
  1. [Sec. 3.1 vs. Sec. 3.4a] The body-conditioned ControlNet is trained only on SMPLX bodies with shape parameters sampled from the Betas space and poses interpolated between T-pose and A-pose (Sec. 3.1). The primary input route described in Sec. 3.4a, however, obtains the target body by PyMAF fitting from a single dressed-human image, which can yield arbitrary poses such as bent elbows, seated postures, or raised arms. This out-of-distribution shift is untested: the quantitative test split is the 10% held-out subset of the same T/A/Betas generation pipeline, so it cannot detect failures on arbitrary pose. Downstream stages cannot correct such misalignment: the Sim(3) optimization in Sec. 3.2 is a global similarity transform, and the XPBD penetration handling in Sec. 3.3 only pushes penetrated vertices out of the body rather than moving an asset to its correct pose-dependent location. The Limitations section (Sec. 5) lists multi-layer garments, mesh topology, SAM occlusion, and hands, but does not mention pose generalization. This is load-bearing for the central claim that assets are 'automatically dressed' on the given 3D human bodies, so the claim should either be narrowed to the trained pose range or supported by experiments on arbitrary fitted bodies.
  2. [Sec. 4, Table 1] The quantitative comparison in Table 1 reports CD/NC/P2S but gives no error bars, no number of test samples, and no details on how the baselines (BCNet, ClothWild, SewFormer, Frankenstein, Garment3DGen) are adapted to the body-aligned task. Without this information it is impossible to assess whether the reported improvements are statistically significant or an artifact of the baseline setup. The paper should state the test set size, the variance across samples, and the exact protocol used for each baseline (e.g., whether they were re-trained, fine-tuned, or used out-of-the-box and reposed for evaluation).
  3. [Sec. 4, Fig. 8] The ablation of the alignment strategy (Sim(3) optimization and penetration handling) is presented only qualitatively in Fig. 8. No quantitative metrics are reported for the final fitted asset-body alignment, such as penetration depth, silhouette IoU, or body-to-asset distance. Since the central contribution includes this alignment stage, the claim that it is essential would be strengthened by numbers, not only by visual examples.
minor comments (5)
  1. [Abstract and Introduction] The text contains several typos: 'achived' should be 'achieved', 'an method' should be 'a method', and 'Wearble' in the Additional Key Words should be 'Wearable'.
  2. [Sec. 2] In the Related Work, 'physcis-based' should be 'physics-based', and 'Juanus-face' should be 'Janus-face'.
  3. [Sec. 3.1] The phrase 'weigths' should be 'weights', and 'bewteen' should be 'between'.
  4. [Sec. 3.3] The text says the simulator 'can hanlde the penetration' – 'hanlde' should be 'handle', and 'simplifiy' should be 'simplify'.
  5. [Sec. 4] In the ablation section, 'Fig.d' should be 'Fig. 8d'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivation is a supervised learning pipeline evaluated on a held-out split, with per-instance alignment post-processing.

full rationale

The paper's derivation chain is not circular. The core claim is that a body-conditioned ControlNet, trained on 70,485 retargeted asset-body pairs, produces body-aligned multiview images that are turned into 3D assets by a native 3D diffusion model, then aligned via Sim(3) optimization and XPBD penetration handling. Each stage is independently specified and none reduces to the claimed output by construction. The ControlNet is supervised on ground-truth multiview renderings of assets that have been retargeted to SMPLX bodies, with XYZ maps as conditioning; this is standard supervised learning, not a self-fulfilling definition. The Sim(3) optimization (Eq. 1-2) is a per-instance similarity fit under silhouette loss, not a fitted parameter that is later renamed a prediction, and the XPBD penetration resolution is a physically motivated post-process. Self-citations (Frankenstein, GarverseLOD, BlockFusion) appear only as related-work references or as baselines and do not carry the central argument; no uniqueness theorem or prior claim is invoked to forbid alternatives. The quantitative evaluation in Table 1 is run on a held-out 10% split of the same asset-body pipeline, which is an in-distribution test rather than a circular reduction: the model still must predict unseen body-aligned shapes and is measured against ground-truth meshes via CD, NC, and P2S. The reviewer's concern about pose generalization from T/A-interpolated training bodies to PyMAF-fitted arbitrary poses is a legitimate out-of-distribution robustness risk, but it is not a circularity: an untested generalization gap is not the same as a prediction being equivalent to its input by construction. Overall, the method is an empirical pipeline with independent external benchmarks and no load-bearing self-referential steps, so the circularity score is 0.

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

The central claim is an empirical system, not a derivation. The method relies on several unverified background assumptions, detailed above, plus external models and datasets that are not released. There are no fitted constants in a formula; the learned weights are the method itself. The most significant unstated assumption is pose generalization.

assumptions (6)
  • domain assumption Stable Diffusion 2.0 weights can be fine-tuned into a consistent multiview image generator following the Zero123++ recipe.
    The paper builds its multiview generator by fine-tuning SD2.0 on Objaverse renders, assuming the Zero123++ approach transfers to their dataset and camera configuration (§3.1).
  • domain assumption Native 3D diffusion models (CLAY, Tripo) generate accurate 3D shapes from input multiview images.
    The pipeline feeds body-aligned multiview images into an off-the-shelf native 3D diffusion model and assumes it faithfully reconstructs the asset shape (§3.2).
  • ad hoc to paper SMPLX random shape parameters and T/A pose interpolation produce a sufficiently diverse and representative set of body-aligned assets for training.
    The dataset of 70,485 pairs is built with this sampling, and generalization to arbitrary bodies is not validated (§3.1).
  • domain assumption ClothCap-based retargeting correctly aligns arbitrary assets to randomly sampled SMPLX bodies.
    The authors rely on this retargeting to produce the body-aligned training pairs; no quantitative check of alignment quality is given (§3.1).
  • domain assumption SAM and Grounded SAM provide accurate silhouettes and masks for the input asset images.
    The Sim(3) optimization uses input silhouettes from SAM, and case workflows use Grounded SAM for segmentation; inaccurate masks would degrade alignment (§3.2, §3.4).
  • domain assumption The XPBD-based penetration solver resolves asset-body penetrations without significantly deforming the asset geometry.
    The paper states gravity is omitted to preserve shape, but does not quantify shape preservation or penetration-free guarantees (§3.3).

how reviews work

0 comments
Cite this review

Pith. "Pith review of BAG: Body-Aligned 3D Wearable Asset Generation." pith.science (2026). https://pith.science/paper/NQBOMG47

@misc{pith2026250116177,
  author       = {Pith},
  title        = {Pith review of: BAG: Body-Aligned 3D Wearable Asset Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NQBOMG47}},
  note         = {Machine review of arXiv:2501.16177}
}
read the original abstract

While recent advancements have shown remarkable progress in general 3D shape generation models, the challenge of leveraging these approaches to automatically generate wearable 3D assets remains unexplored. To this end, we present BAG, a Body-aligned Asset Generation method to output 3D wearable asset that can be automatically dressed on given 3D human bodies. This is achived by controlling the 3D generation process using human body shape and pose information. Specifically, we first build a general single-image to consistent multiview image diffusion model, and train it on the large Objaverse dataset to achieve diversity and generalizability. Then we train a Controlnet to guide the multiview generator to produce body-aligned multiview images. The control signal utilizes the multiview 2D projections of the target human body, where pixel values represent the XYZ coordinates of the body surface in a canonical space. The body-conditioned multiview diffusion generates body-aligned multiview images, which are then fed into a native 3D diffusion model to produce the 3D shape of the asset. Finally, by recovering the similarity transformation using multiview silhouette supervision and addressing asset-body penetration with physics simulators, the 3D asset can be accurately fitted onto the target human body. Experimental results demonstrate significant advantages over existing methods in terms of image prompt-following capability, shape diversity, and shape quality. Our project page is available at https://bag-3d.github.io/.

Figures

Figures reproduced from arXiv: 2501.16177 by the authors.

Figure 1
Figure 1. BAG generates a variety of body-aligned 3D shapes, ranging from individual wearable assets to combinations of multiple components. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Method Pipeline. Given an input image and a target body, we employ body-conditioned image generation to produce body-aligned consistent four-view orthographic images (see Sec. 3.1). The four-view images are then fed into a native 3D diffusion model to obtain the asset shape. The similarity transformation (Sim3) of the generated asset is estimated through silhouette-based projection optimization (see Sec. 3.2). Final… view at source ↗
Figure 3
Figure 3. Canonical body space (left), and examples of body-aligned 3D asset [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (7 more)
Figure 5
Figure 5. Figure 5: Four Methods to acqure input body and image pairs. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Qualitative asset shape generation results. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Ablation study of body-aligned multi-view generation. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Qualitative comparison between ours and the ablated alignement strategies. As shown, while the generated multi-view images align with the rendered [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Result gallery across four settings: (a) Single-view reconstruction with fitted SMPLX; (b) Image-Based Virtual Try-On; (c) Assembling existing 2D assets; [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Qualitative comparison between ours and the state of the arts. For each row, the input image is followed by the results generated by BCNet [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 11
Figure 11. Figure 11: Qualitative comparison on multi-view generation. For each case, the input image and body condition are followed by the results generated by [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

81 extracted references · 51 canonical work pages

  1. [1]

    Dresscode: Autoregressively sewing and generating garments from text guidance

    Kai He, Kaixin Yao, Qixuan Zhang, Jingyi Yu, Lingjie Liu, and Lan Xu. Dresscode: Autoregressively sewing and generating garments from text guidance. ACM Transactions on Graphics (TOG) , 43(4):1–13, 2024

    2024

  2. [2]

    Bcnet: Learning body and cloth shape from a single image

    Boyi Jiang, Juyong Zhang, Yang Hong, Jinhao Luo, Ligang Liu, and Hujun Bao. Bcnet: Learning body and cloth shape from a single image. InEuropean Conference on Computer Vision. Springer, 2020

    work page 2020

  3. [3]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems , 30, 2017

    2017

  4. [4]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems , 33:6840–6851, 2020

    2020

  5. [5]

    Dreamfusion: Text-to-3d using 2d diffusion

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022

    arXiv 2022

  6. [6]

    Lrm: Large reconstruction model for single image to 3d

    Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023

    arXiv 2023

  7. [7]

    Clay: A controllable large-scale generative model for creating high-quality 3d assets

    Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG) , 43(4):1–20, 2024

    work page 2024

  8. [8]

    Structured 3d latents for scalable and versatile 3d generation

    Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. arXiv preprint arXiv:2412.01506, 2024

    arXiv 2024

Show all 81 references
  1. [9]

    Meshtron: High- fidelity, artist-like 3d mesh generation at scale

    Zekun Hao, David W Romero, Tsung-Yi Lin, and Ming-Yu Liu. Meshtron: High- fidelity, artist-like 3d mesh generation at scale. arXiv preprint arXiv:2412.09548, 2024

    2024 arXiv

  2. [10]

    Garment3dgen: 3d garment stylization and texture generation

    Nikolaos Sarafianos, Tuur Stuyck, Xiaoyu Xiang, Yilei Li, Jovan Popovic, and Rakesh Ranjan. Garment3dgen: 3d garment stylization and texture generation. arXiv preprint arXiv:2403.18816, 2024

    2024 arXiv

  3. [11]

    Zero123++: a single image to consistent multi-view diffusion base model, 2023

    Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model, 2023

    2023

  4. [12]

    Objaverse-xl: A universe of 10m+ 3d objects

    Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objaverse-xl: A universe of 10m+ 3d objects. arXiv preprint arXiv:2307.05663 , 2023

    2023 arXiv

  5. [13]

    Adding conditional control to text-to-image diffusion models, 2023

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models, 2023

    2023

  6. [14]

    Generative adversarial net- works

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial net- works. Communications of the ACM, 63(11):139–144, 2020

    2020

  7. [15]

    Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling

    Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. Advances in neural information processing systems , 29, 2016

    2016

  8. [16]

    Efficient geometry-aware 3d generative adversarial networks

    Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. InProceedings of the IEEE/CVF Conference on Computer Visi...

    2022

  9. [17]

    Get3d: A generative model of high quality 3d textured shapes learned from images

    Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler. Get3d: A generative model of high quality 3d textured shapes learned from images. Advances In Neural Information Processing Systems, 35:31841–31854, 2022

    2022

  10. [18]

    Magic3d: High- resolution text-to-3d content creation

    Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High- resolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti...

    2023

  11. [19]

    Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation

    Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. InProceed- ings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 22246–22256, October 2023

    2023

  12. [20]

    Point-e: A system for generating 3d point clouds from complex prompts

    Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022

    2022 arXiv

  13. [21]

    Lion: Latent point diffusion models for 3d shape generation

    Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. Lion: Latent point diffusion models for 3d shape generation. arXiv preprint arXiv:2210.06978, 2022

    2022 arXiv

  14. [22]

    Black, Derek Nowrouzezahrai, Liam Paull, and Weiyang Liu

    Zhen Liu, Yao Feng, Michael J. Black, Derek Nowrouzezahrai, Liam Paull, and Weiyang Liu. Meshdiffusion: Score-based generative 3d mesh modeling. In International Conference on Learning Representations , 2023

    2023

  15. [23]

    Locally attentional sdf diffusion for controllable 3d shape generation

    Xin-Yang Zheng, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu, and Heung- Yeung Shum. Locally attentional sdf diffusion for controllable 3d shape generation. arXiv preprint arXiv:2305.04461, 2023

    2023 arXiv

  16. [24]

    Xcube: Large-scale 3d generative modeling using sparse voxel hierar- chies

    Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams. Xcube: Large-scale 3d generative modeling using sparse voxel hierar- chies. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4209–4219, 2024

    2024

  17. [25]

    Octfusion: Octree-based diffusion models for 3d shape generation

    Bojun Xiong, Si-Tong Wei, Xin-Yang Zheng, Yan-Pei Cao, Zhouhui Lian, and Peng- Shuai Wang. Octfusion: Octree-based diffusion models for 3d shape generation. arXiv preprint arXiv:2408.14732, 2024

    2024 arXiv

  18. [26]

    Rodin: A generative model for sculpting 3d digital avatars using diffusion

    Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Bal- trusaitis, Jingjing Shen, Dong Chen, Fang Wen, Qifeng Chen, et al. Rodin: A generative model for sculpting 3d digital avatars using diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision a...

    2023

  19. [27]

    3d neural field generation using triplane diffusion

    J Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, and Gordon Wetzstein. 3d neural field generation using triplane diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 20875–20886, 2023

    2023

  20. [28]

    Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction

    Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, and Hao Su. Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction. arXiv preprint arXiv:2304.06714, 2023

    2023 arXiv

  21. [29]

    Blockfusion: Expandable 3d scene generation using latent tri-plane extrapolation

    Zhennan Wu, Yang Li, Han Yan, Taizhang Shang, Weixuan Sun, Senbo Wang, Ruikai Cui, Weizhe Liu, Hiroyuki Sato, Hongdong Li, and Pan Ji. Blockfusion: Expandable 3d scene generation using latent tri-plane extrapolation. ACM Trans- actions on Graphics, 43(4), 2024

    2024

  22. [30]

    3dtopia-xl: High-quality 3d pbr asset generation via primitive diffusion

    Zhaoxi Chen, Jiaxiang Tang, Yuhao Dong, Ziang Cao, Fangzhou Hong, Yushi Lan, Tengfei Wang, Haozhe Xie, Tong Wu, Shunsuke Saito, Liang Pan, Dahua Lin, and Ziwei Liu. 3dtopia-xl: High-quality 3d pbr asset generation via primitive diffusion. arXiv preprint arXiv:2409.12957, 2024

    2024 arXiv

  23. [31]

    3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models.ACM Transactions on Graphics (TOG) , 42(4):1–16, 2023

    Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models.ACM Transactions on Graphics (TOG) , 42(4):1–16, 2023

    2023

  24. [32]

    Hyper- diffusion: Generating implicit neural fields with weight-space diffusion

    Ziya Erkoç, Fangchang Ma, Qi Shan, Matthias Nießner, and Angela Dai. Hyper- diffusion: Generating implicit neural fields with weight-space diffusion. arXiv preprint arXiv:2303.17015, 2023

    2023 arXiv

  25. [33]

    Polygen: An autoregressive generative model of 3d meshes

    Charlie Nash, Yaroslav Ganin, SM Ali Eslami, and Peter Battaglia. Polygen: An autoregressive generative model of 3d meshes. In International conference on machine learning, pages 7220–7229. PMLR, 2020

    2020

  26. [34]

    Meshgpt: Generating triangle meshes with decoder-only transformers

    Yawar Siddiqui, Antonio Alliegro, Alexey Artemov, Tatiana Tommasi, Daniele Sirigatti, Vladislav Rosov, Angela Dai, and Matthias Nießner. Meshgpt: Generating triangle meshes with decoder-only transformers. arXiv preprint arXiv:2311.15475, 2023

    2023 arXiv

  27. [35]

    Edgerunner: Auto-regressive auto-encoder for artistic mesh generation

    Jiaxiang Tang, Zhaoshuo Li, Zekun Hao, Xian Liu, Gang Zeng, Ming-Yu Liu, and Qinsheng Zhang. Edgerunner: Auto-regressive auto-encoder for artistic mesh generation. arXiv preprint arXiv:2409.18114, 2024

    2024 arXiv

  28. [36]

    Scaling mesh generation via compressive tokenization

    Haohan Weng, Zibo Zhao, Biwen Lei, Xianghui Yang, Jian Liu, Zeqiang Lai, Zhuo Chen, Yuhong Liu, Jie Jiang, Chunchao Guo, et al. Scaling mesh generation via compressive tokenization. arXiv preprint arXiv:2411.07025, 2024

    2024 arXiv

  29. [37]

    Wonder3d: Single image to 3d using cross-domain diffusion

    Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. arXiv preprint arXiv:2310.15008, 2023

    arXiv

  30. [38]

    Towards garment sewing pattern reconstruction from a single image

    Lijuan Liu, Xiangyu Xu, Zhijie Lin, Jiabin Liang, and Shuicheng Yan. Towards garment sewing pattern reconstruction from a single image. ACM Transactions on Graphics (SIGGRAPH Asia) , 2023

    2023

  31. [39]

    David Baraff and Andrew P. Witkin. Large steps in cloth simulation. Proceedings of the 25th annual conference on Computer graphics and interactive techniques , 1998

    1998

  32. [40]

    Drapenet: Garment generation and self-supervised draping

    Luca De Luigi, Ren Li, Benoit Guillard, Mathieu Salzmann, and Pascal Fua. Drapenet: Garment generation and self-supervised draping. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1451–1460, 2023

    2023

  33. [41]

    Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. SMPL: A skinned multi-person linear model. ACM Trans. Graphics (Proc. SIGGRAPH Asia) , 34(6):248:1–248:16, October 2015

    2015

  34. [42]

    Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, and Michael J. Black. Learning to Dress 3D People in Generative Clothing. In Computer Vision and Pattern Recognition (CVPR) , 2020

    2020

  35. [43]

    Smplicit: Topology-aware generative model for clothed people

    Enric Corona, Albert Pumarola, Guillem Alenyà, Gerard Pons-Moll, and Francesc Moreno-Noguer. Smplicit: Topology-aware generative model for clothed people. In CVPR, 2021

    2021

  36. [44]

    Neuraltailor: Reconstructing sewing pattern structures from 3d point clouds of garments

    Maria Korosteleva and Sung-Hee Lee. Neuraltailor: Reconstructing sewing pattern structures from 3d point clouds of garments. ACM Trans. Graph., 41(4), 2022

    2022

  37. [45]

    Multi-garment net: Learning to dress 3d people from images

    Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, and Gerard Pons-Moll. Multi-garment net: Learning to dress 3d people from images. In Proceedings of the IEEE/CVF international conference on computer vision , pages 5420–5430, 2019

    2019

  38. [46]

    Cloth3d: clothed 3d humans

    Hugo Bertiche, Meysam Madadi, and Sergio Escalera. Cloth3d: clothed 3d humans. In European Conference on Computer Vision , pages 344–359. Springer, 2020. BAG: Body-Aligned 3D Wearable Asset Generation • 11

    2020

  39. [47]

    Garverselod: High-fidelity 3d garment reconstruction from a single in-the-wild image using a dataset with levels of details

    Zhongjin Luo, Haolin Liu, Chenghong Li, Wanghao Du, Zirong Jin, Wanhu Sun, Yinyu Nie, Weikai Chen, and Xiaoguang Han. Garverselod: High-fidelity 3d garment reconstruction from a single in-the-wild image using a dataset with levels of details. ACM Transactions on Graphics (TOG) , 2024

    2024

  40. [48]

    Bergman, Petr Kellnhofer, Wang Yifan, Eric R

    Alexander W. Bergman, Petr Kellnhofer, Wang Yifan, Eric R. Chan, David B. Lindell, and Gordon Wetzstein. Generative neural articulated radiance fields. In NeurIPS, 2022

    2022

  41. [49]

    gdna: Towards generative detailed neural avatars

    Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 20427–20437, 2022

    2022

  42. [50]

    Unsupervised learning of efficient geometry-aware neural articulated representations

    Atsuhiro Noguchi, Xiao Sun, Stephen Lin, and Tatsuya Harada. Unsupervised learning of efficient geometry-aware neural articulated representations. In Euro- pean Conference on Computer Vision , pages 597–614. Springer, 2022

    2022

  43. [51]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sand- hini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In Inter- national conference on machine learning...

    2021

  44. [52]

    High-resolution image synthesis with latent diffusion models

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

    2022

  45. [53]

    Avatarclip: Zero-shot text-driven generation and animation of 3d avatars

    Fangzhou Hong, Mingyuan Zhang, Liang Pan, Zhongang Cai, Lei Yang, and Ziwei Liu. Avatarclip: Zero-shot text-driven generation and animation of 3d avatars. ACM Transactions on Graphics (TOG) , 41(4):1–19, 2022

    2022

  46. [54]

    Dreama- vatar: Text-and-shape guided 3d human avatar generation via diffusion models

    Yukang Cao, Yan-Pei Cao, Kai Han, Ying Shan, and Kwan-Yee K Wong. Dreama- vatar: Text-and-shape guided 3d human avatar generation via diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 958–968, 2024

    2024

  47. [55]

    Tingting Liao, Hongwei Yi, Yuliang Xiu, Jiaxiang Tang, Yangyi Huang, Justus Thies, and Michael J. Black. TADA! Text to Animatable Digital Avatars. In International Conference on 3D Vision (3DV) , 2024

    2024

  48. [56]

    Humannorm: Learning normal diffusion model for high-quality and realistic 3d human generation, 2024

    Xin Huang, Ruizhi Shao, Qi Zhang, Hongwen Zhang, Ying Feng, Yebin Liu, and Qing Wang. Humannorm: Learning normal diffusion model for high-quality and realistic 3d human generation, 2024

    2024

  49. [57]

    Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization

    Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In The IEEE International Conference on Computer Vision (ICCV), October 2019

    2019

  50. [58]

    Pamir: Parametric model- conditioned implicit representation for image-based human reconstruction

    Zerong Zheng, Tao Yu, Yebin Liu, and Qionghai Dai. Pamir: Parametric model- conditioned implicit representation for image-based human reconstruction. IEEE transactions on pattern analysis and machine intelligence , 44(6):3170–3184, 2021

    2021

  51. [59]

    Yuliang Xiu, Jinlong Yang, Dimitrios Tzionas, and Michael J. Black. ICON: Implicit Clothed humans Obtained from Normals. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR) , pages 13296–13306, June 2022

    2022

  52. [60]

    Charactergen: Efficient 3d character generation from single images with multi-view pose canonicalization

    Hao-Yang Peng, Jia-Peng Zhang, Meng-Hao Guo, Yan-Pei Cao, and Shi-Min Hu. Charactergen: Efficient 3d character generation from single images with multi-view pose canonicalization. ACM Transactions on Graphics (TOG) , 43(4), 2024

    2024

  53. [61]

    Clothcap: Seam- less 4d clothing capture and retargeting

    Gerard Pons-Moll, Sergi Pujades, Sonny Hu, and Michael Black. Clothcap: Seam- less 4d clothing capture and retargeting. ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4), 2017. Two first authors contributed equally

    2017

  54. [62]

    Yao Feng, Weiyang Liu, Timo Bolkart, Jinlong Yang, Marc Pollefeys, and Michael J. Black. Learning disentangled avatars with hybrid 3d representations. arXiv, 2023

    2023

  55. [63]

    Hao Zhang, Yao Feng, Peter Kulits, Yandong Wen, Justus Thies, and Michael J. Black. Teca: Text-guided generation and editing of compositional 3d avatars. arXiv, 2023

    2023

  56. [64]

    Disentangled clothed avatar generation from text descriptions, 2023

    Jionghao Wang, Yuan Liu, Zhiyang Dou, Zhengming Yu, Yongqing Liang, Xin Li, Wenping Wang, Rong Xie, and Li Song. Disentangled clothed avatar generation from text descriptions, 2023

    2023

  57. [65]

    Frankenstein: Generating semantic-compositional 3d scenes in one tri-plane

    Han Yan, Yang Li, Zhennan Wu, Shenzhou Chen, Weixuan Sun, Taizhang Shang, Weizhe Liu, Tian Chen, Xiaqiang Dai, Chao Ma, et al. Frankenstein: Generating semantic-compositional 3d scenes in one tri-plane. In SIGGRAPH Asia 2024 Conference Papers, pages 1–11, 2024

    2024

  58. [66]

    Stdgen: Semantic-decomposed 3d character generation from single images, 2024

    Yuze He, Yanning Zhou, Wang Zhao, Zhongkai Wu, Kaiwen Xiao, Wei Yang, Yong- Jin Liu, and Xiao Han. Stdgen: Semantic-decomposed 3d character generation from single images, 2024

    2024

  59. [67]

    Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, and Michael J. Black. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2019

    2019

  60. [68]

    Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. Segment anything. arXiv:2304.02643, 2023

    2023 arXiv

  61. [69]

    Occupancy networks: Learning 3d reconstruction in function space

    Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460–4470, 2019

    2019

  62. [70]

    Proxy asset generation for cloth simulation in games

    Zhongtian Zheng, Tongtong Wang, Qijia Feng, Zherong Pan, Xifeng Gao, and Kui Wu. Proxy asset generation for cloth simulation in games. ACM Transactions on Graphics (TOG), 43(4):1–12, 2024

    2024

  63. [71]

    Generic remeshing of 3d triangular meshes with metric-dependent discrete voronoi diagrams

    Sébastien Valette, Jean Marc Chassery, and Rémy Prost. Generic remeshing of 3d triangular meshes with metric-dependent discrete voronoi diagrams. IEEE Transactions on Visualization and Computer Graphics , 14(2):369–381, 2008

    2008

  64. [72]

    Xpbd: Position- based simulation of compliant constrained dynamics

    Miles Macklin, Matthias Müller, and Nuttapong Chentanez. Xpbd: Position- based simulation of compliant constrained dynamics. In Proceedings of the 9th International Conference on Motion in Games , pages 49–54, 2016

    2016

  65. [73]

    Optimized spatial hashing for collision detection of deformable objects

    Matthias Teschner, Bruno Heidelberger, Matthias Müller, Danat Pomeranets, and Markus Gross. Optimized spatial hashing for collision detection of deformable objects. In Proceedings of Vision, Modeling, and Visualization (VMV) , pages 47–54, 2003

    2003

  66. [74]

    Pymaf: 3d human pose and shape regression with pyramidal mesh alignment feedback loop

    Hongwen Zhang, Yating Tian, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, and Zhenan Sun. Pymaf: 3d human pose and shape regression with pyramidal mesh alignment feedback loop. In Proceedings of the IEEE International Conference on Computer Vision , 2021

    2021

  67. [75]

    Pymaf-x: Towards well-aligned full-body model regression from monocular images

    Hongwen Zhang, Yating Tian, Yuxiang Zhang, Mengcheng Li, Liang An, Zhenan Sun, and Yebin Liu. Pymaf-x: Towards well-aligned full-body model regression from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

    2023

  68. [76]

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

    Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453, 2023

    2023 arXiv

  69. [77]

    Kolors: Effective training of diffusion model for photorealistic text-to-image synthesis

    Kolors Team. Kolors: Effective training of diffusion model for photorealistic text-to-image synthesis. arXiv preprint, 2024

    2024

  70. [78]

    Grounded sam: Assembling open-world models for diverse visual tasks, 2024

    Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded sam: Assembling open-world models for diverse visual tasks, 2024

    2024

  71. [79]

    The unreasonable effectiveness of deep features as a perceptual metric

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

    2018

  72. [80]

    3d clothed human reconstruction in the wild

    Gyeongsik Moon, Hyeongjin Nam, Takaaki Shiratori, and Kyoung Mu Lee. 3d clothed human reconstruction in the wild. In European Conference on Computer Vision (ECCV), 2022

    2022

  73. [81]

    Frankenstein: Generating semantic-compositional 3d scenes in one tri-plane.arXiv preprint arXiv:2403.16210, 2024

    Han Yan, Yang Li, Zhennan Wu, Shenzhou Chen, Weixuan Sun, Taizhang Shang, Weizhe Liu, Tian Chen, Xiaqiang Dai, Chao Ma, et al. Frankenstein: Generating semantic-compositional 3d scenes in one tri-plane.arXiv preprint arXiv:2403.16210, 2024

    2024 arXiv

Pith tools

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