OmniFabric is a new method for creating high‑quality, globally coherent texture maps for 3D garment reconstruction from a single image. It first generates a coarse texture initialization on the garment’s sewing pattern using a 3D mesh and Vision‑Language Model priors, then refines this in the UV domain with a diffusion transformer conditioned on 3D positional features. The approach removes distortion and baked‑in artifacts, producing photorealistic 3D garments that outperform existing baselines.
OmniFabric is a new method for creating production‑ready 3D garment assets from a single image. It generates globally coherent texture maps directly in the 2D sewing pattern (UV) space, using a coarse initialization from Vision‑Language Models and refining it with a diffusion transformer conditioned on 3D positional features. The approach removes distortion and baked‑in artifacts, producing photorealistic 3D garments with high‑quality textures that outperform current state‑of‑the‑art baselines.
By Ding-Jiun Huang, Yuanhao Wang, Cheng Zhang, Hugo Bertiche, Alexandru-Eugen Ichim, Thabo Beeler, Fernando De la Torre
arXiv:2609.18510v1 Announce Type: new
Abstract: We present DiT-Garment to model dynamic 3D clothing over human body models in arbitrary motion. Unlike existing methods, DiT-Garment can animate garmen...
By Antoine Dumoulin, Laurence Boissieux, Joao Regateiro, Pierre Hellier, Stefanie Wuhrer
Creating photorealistic and temporally coherent animatable human avatars from RGB videos remains challenging. Current methods struggle to capture realistic cloth dynamics, producing over-smoothed appearance or severe artifacts on out-of-distribution poses.
arXiv:2610.19623v1 Announce Type: new
Abstract: Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layere...
By Jia Chen, Ruyang Zhao, Jun Bai, Chao Huang, Saishang Zhong, Xinrong Hu
arXiv:2511. 18765v3 Announce Type: replace-cross Abstract: Existing industrial 3D garment meshes already cover most real-world clothing geometries, yet their texture diversity remains limited.
By Hui Shan, Ming Li, Haitao Yang, Kai Zheng, Sizhe Zheng, Yanwei Fu, Xiangru Huang
arXiv:2609.39335v1 Announce Type: new
Abstract: Video virtual try-on has attracted increasing attention due to its broad potential in digital fashion and intelligent e-commerce. However, existing met...
By Zijing Qin, Jun Zhou, Ruicheng Zhang, Jiaqi Hou, Zunnan Xu, Ronghui Li, Zhenyu Xie, Xiu Li
BooM‑VVT is a mask‑free video virtual try‑on framework that builds on a keyframe‑driven paradigm. It introduces a multi‑stage training strategy using image‑level pseudo data to learn mask‑free localization, a garment‑sensitive keyframe sampling method to capture garment appearance, and a Frame‑Shared 3D‑RoPE module to align keyframes with target video frames for accurate garment detail transfer. The authors also release OmniView, a large‑scale multi‑view try‑on dataset, and demonstrate that BooM‑VVT outperforms existing methods in temporal consistency and garment fidelity.
By Wei Zhang, Xin Li, Peishu Shi, Jialin Gao, Xuekang Peng, Zhichao Lian, Yeying Jin
BooM-VVT is a mask‑free video virtual try‑on framework that builds on a keyframe‑driven paradigm. It uses a multi‑stage training strategy with image‑level pseudo data to learn mask‑free localization, introduces Garment‑Sensitive Keyframe Sampling to capture garment appearance, and employs Frame‑Shared 3D‑RoPE for spatiotemporal correspondence. The authors also create the OmniView dataset to support diverse camera viewpoints and tasks, achieving superior temporal consistency and garment fidelity compared to existing methods.
arXiv:2607.11233v2 Announce Type: replace
Abstract: Virtual try-on (VTON) is a bi-conditional image generation problem that requires not only accurate person preservation but also faithful garment de...
By Lu Yang, Xiaonan Hu, Yanan Li, Daqi Liu, Hao Lu, Xiang Bai
arXiv:2610.15582v1 Announce Type: new
Abstract: We study the problem of inverse garment design, reconstructing simulation-ready 3D outfits with arbitrary layers from a single image, with visual agent...
By Nanxiang Jiang, Yunuo Chen, Zeng Tao, Zizhang Li, Qi Guo, Jiajun Wu, Yin Yang, Chenfanfu Jiang
The paper introduces HyperBones, a real‑time garment simulation framework that combines a reduced‑space neural dynamics simulator with a lightweight neural network correcting Linear Blend Skinning (LBS) at a coarse level, and a convolutional MLP for fine‑scale wrinkle recovery in UV space. By decoupling identity‑specific computation from shape conditioning through a hypernetwork, the method achieves high performance without an offline simulator, delivering physically plausible dynamics across diverse motions and unseen body shapes. Experiments demonstrate a speedup of over 30× compared to state‑of‑the‑art autoregressive neural simulators, reaching interactive inference at roughly 1 ms per frame on a consumer GPU.
By Astitva Srivastava, Hsiao-Yu Chen, Ryan Goldade, Philipp Herholz, Zhongshi Jiang, Gene Wei-Chin Lin, Lingchen Yang, Nikolaos Sarafianos, Tuur Stuyck, Avinash Sharma, Egor Larionov