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: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...
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...
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...
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.
Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions. However, existing evaluations of physical fidelity are often conducted in isolation and rely heavily on perceptual similarity or human judgments, providing limited insight into which physical principles or parameters are violated.
arXiv:2610.33492v1 Announce Type: new Abstract: Graph neural networks (GNNs) simulate garments efficiently through physics-based self-supervision, but leave body-garment friction implicit or inside t...
arXiv:2608. 05948v1 Announce Type: new Abstract: Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions.
Graph neural networks (GNNs) simulate garments efficiently through physics-based self-supervision, but leave body-garment friction implicit or inside training energies, with no friction coefficient $μ...
FitControler introduces a fit-aware virtual try‑on system that adds garment fit control to existing VTON models. It uses a fit‑aware layout generator and a multi‑scale fit injector to redraw body‑garment layouts and render garments that match those layouts. The authors also release a new Fit4Men dataset of 13,000 body‑garment pairs and two fit consistency metrics to evaluate fit quality.
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.
The paper introduces GALA, a distillation technique that replaces costly neural decoding in 3D Gaussian avatars with a shallow MLP predicting blendshape coefficients, enabling real‑time animation. By constructing a basis via block‑local PCA under a rendering‑aware metric, GALA achieves high fidelity while reducing memory usage. Experiments on three avatar models show up to three orders of magnitude lower CPU cost and frame rates up to 60fps on mobile devices.
The paper introduces two learning-based methods that automate the creation of digital garments from 2D vector patterns. One method predicts sewing operations at the segment level using a Transformer encoder, handling complex seam relationships and construction details. The other predicts panel placement for drape simulation, assigning body regions, layers, and coordinates. Both models achieve significant accuracy on a dataset of 4,035 garments and are integrated into garment CAD software for automatic assembly.