DiT-Garment: Garment Dynamics with Diffusion Transformers
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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...
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.
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.
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.
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.
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.