HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning

• URL: http://arxiv.org/abs/2605.20460v2
• Date: Wed, 27 May 2026 11:24:44 GMT
• Title: HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning
• Abstract summary: We present a fast and physically plausible approach for dynamic garment simulation.<n>Our method trains a reduced-space neural dynamics simulator composed of independent coarse- and fine-level components.<n>Our simulator runs at 300+ FPS on a commodity GPU, making it suitable for real-time applications.
• Score: 19.763569928485868
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in garment simulation have brought high-quality results closer to real-time performance. Physics-based simulators can produce accurate motion, but remain too computationally expensive for interactive applications. In contrast, linear blend skinning is efficient, but cannot capture the complex dynamics of loose-fitting garments, often leading to unrealistic motion and visual artifacts. Neural methods offer a promising alternative, yet they still struggle to animate loose clothing plausibly under strict runtime constraints. We present a fast and physically plausible approach for dynamic garment simulation. Our method trains a reduced-space neural dynamics simulator composed of independent coarse- and fine-level components. At the coarse level, the garment is driven by a set of virtual bones integrated with a lightweight neural network. Fine-scale wrinkle details are then recovered using a trained convolutional neural map. By decoupling identity-specific computation from real-time neural integration, our architecture maintains high performance while supporting diverse body shapes and motions. We further introduce an effective physics-supervision scheme that enables accurate results without relying on an external simulator. Experiments show that our method produces physically plausible garment dynamics, generalizes across a range of motions and body shapes, and supports a fixed set of garments. Our simulator runs at 300+ FPS on a commodity GPU, making it suitable for real-time applications.

Related papers

• GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning [55.272458304179025]
  GS-Playground is a multi-modal simulation framework designed to accelerate end-to-end perceptual learning.<n>Our system achieves a breakthrough throughput of 104 FPS at 640x480 resolution, significantly lowering the barrier for large-scale visual RL.
  arXiv  Detail & Related papers  (2026-04-28T10:05:39Z)
• DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer [62.18680935878919]
  We introduce DiffusionHarmonizer, an online generative enhancement framework that transforms renderings into temporally consistent outputs.<n>At its core is a single-step temporally-conditioned enhancer capable of running in online simulators on a single GPU.
  arXiv  Detail & Related papers  (2026-02-27T15:35:30Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• Realistic Surgical Simulation from Monocular Videos [49.114370617718244]
  This paper tackles the challenge of automatically performing realistic surgical simulations from readily available surgical videos.<n>We propose SurgiSim, a novel automatic simulation system to overcome these limitations.<n> Experiments on various surgical scenarios and interactions demonstrate SurgiSim's ability to perform realistic simulation of soft tissues.
  arXiv  Detail & Related papers  (2024-12-03T10:32:41Z)
• Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics [48.99021224773799]
  We propose the Neural Material Adaptor (NeuMA), which integrates existing physical laws with learned corrections. We also propose Particle-GS, a particle-driven 3D Gaussian Splatting variant that bridges simulation and observed images.
  arXiv  Detail & Related papers  (2024-10-10T17:43:36Z)
• A Physics-embedded Deep Learning Framework for Cloth Simulation [6.8806198396336935]
  This paper proposes a physics-embedded learning framework that directly encodes physical features of cloth simulation.<n>The framework can also integrate with other external forces and collision handling through either traditional simulators or sub neural networks.
  arXiv  Detail & Related papers  (2024-03-19T15:21:00Z)
• AniDress: Animatable Loose-Dressed Avatar from Sparse Views Using Garment Rigging Model [58.035758145894846]
  We introduce AniDress, a novel method for generating animatable human avatars in loose clothes using very sparse multi-view videos. A pose-driven deformable neural radiance field conditioned on both body and garment motions is introduced, providing explicit control of both parts. Our method is able to render natural garment dynamics that deviate highly from the body and well to generalize to both unseen views and poses.
  arXiv  Detail & Related papers  (2024-01-27T08:48:18Z)
• DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation [27.553678582454648]
  Physical simulations can produce realistic motions for clothed humans, but they require high-quality garment assets with associated physical parameters for cloth simulations. We propose papername,a novel approach that performs body and garment co-optimization using differentiable simulation. Our experiments demonstrate that our approach generates realistic clothing and body shape suitable for downstream applications.
  arXiv  Detail & Related papers  (2023-11-20T21:20:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.