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:2602.24043v2 Announce Type: replace
Abstract: Reconstructing 3D clothed humans from monocular images and videos is a fundamental problem with applications in virtual try-on, avatar creation, an...
By Yingxuan You, Ren Li, Corentin Dumery, Cong Cao, Hao Li, Pascal Fua
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:2609.18510v1 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
arXiv:2607. 23189v1 Announce Type: cross Abstract: AI-generated content (AIGC) has made significant progress, with 2D generative models becoming ready-to-use tools for the digital fashion industry.
By Shenghao Yang, Hongtao Zhang, Yuhan Yi, Zhihao Tang, Zihao Cui, Lian Wen, Han Yan, Yuan Gao, Mingbo Zhao
arXiv:2609.10363v1 Announce Type: new
Abstract: SceneHI is a framework that lifts high-resolution, illumination-aware priors from 2D diffusion models to perform 3D texture synthesis. It is the first...
By Athanasios Tragakis, Marco Aversa, Daniela Ivanova, Chaitanya Kaul, Roderick Murray-Smith, Daniele Faccio, Paul Henderson
GarmentWeaver is a new framework for multimodal sewing pattern generation that uses a schema‑aware approach to construct compact hierarchical targets. By activating garment‑relevant structural branches and building on a pretrained vision‑language model, it predicts executable sewing patterns in a structured manner. Experiments show that GarmentWeaver produces more accurate, executable patterns and yields better simulation results than strong baselines.
By Yinwen Lu, Weihao Luo, Yueqi Zhong
The paper introduces AvaImg, a multi‑stage optimization pipeline that achieves high‑fidelity SMPL(-X)+D registrations with UV texture for arbitrary clothed scans. By enforcing a body‑inside‑clothing constraint through signed winding numbers and employing a three‑level efficiency cascade, AvaImg significantly reduces runtime and storage while recovering fine surface detail via coarse‑to‑fine displacement optimization. The resulting textured registrations are nearly indistinguishable from scans, and encoding the UV maps with a frozen FLUX VAE demonstrates compatibility with 2D generative models, enabling 3D avatar generation using image‑based priors.
By Margaret Kostyrko, Yuxuan Xue, Garvita Tiwari, Gerard Pons-Moll
DirectUV is an image-conditioned UV texture diffusion framework that generates high-quality textures directly in UV space using a pretrained image VAE and a Diffusion Transformer. It introduces Surface-Aware Positional Encoding (SAPE), which replaces standard 2D-grid positional encoding with encodings derived from 3D surface coordinates, enabling attention to operate based on surface proximity rather than UV-grid distance. A multi-level extension assigns different attention heads to progressively finer subdivisions of the UV patch, allowing the model to reason about surface structure at multiple granularities, resulting in sharper and more globally consistent textures, especially in occluded and view-unseen regions.
By Jiantao Lin, Yingjie Xu, Mingzhi Sheng, Yangkai Wei, Hao Chen, Ying-Cong Chen
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
PoseDreamer is a new pipeline that uses diffusion models to generate large‑scale synthetic datasets for 3D human mesh estimation, providing 3D mesh annotations that remain aligned with the generated images. The system incorporates controllable image generation, Direct Preference Optimization for control alignment, curriculum‑based hard sample mining, and multi‑stage quality filtering to produce over 500,000 high‑quality samples with a 76% improvement in image‑quality metrics over traditional rendering‑based datasets. Models trained on PoseDreamer match or surpass those trained on real‑world or conventional synthetic data, and combining PoseDreamer with synthetic datasets yields better performance than mixing real and synthetic data alone.
By Lorenza Prospero, Orest Kupyn, Ostap Viniavskyi, Jo\~ao F. Henriques, Christian Rupprecht
arXiv:2404.17569v4 Announce Type: replace
Abstract: This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to gen...
By Shangzhan Zhang, Sida Peng, Tao Xu, Yuanbo Yang, Tianrun Chen, Nan Xue, Yujun Shen, Hujun Bao, Ruizhen Hu, Xiaowei Zhou