A Repulsive Force Unit for Garment Collision Handling in Neural Networks

• URL: http://arxiv.org/abs/2207.13871v1
• Date: Thu, 28 Jul 2022 03:46:16 GMT
• Title: A Repulsive Force Unit for Garment Collision Handling in Neural Networks
• Authors: Qingyang Tan, Yi Zhou, Tuanfeng Wang, Duygu Ceylan, Xin Sun, Dinesh Manocha
• Abstract summary: We propose a novel collision handling neural network layer called Repulsive Force Unit (ReFU) Based on the signed distance function (SDF) of the underlying body, ReFU predicts the per-vertex offsets that push any interpenetrating to a collision-free configuration while preserving the fine geometric details. Our experiments show that ReFU significantly reduces the number of collisions between the body and the garment and better preserves geometric details compared to prior methods.
• Score: 61.34646212450137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite recent success, deep learning-based methods for predicting 3D garment deformation under body motion suffer from interpenetration problems between the garment and the body. To address this problem, we propose a novel collision handling neural network layer called Repulsive Force Unit (ReFU). Based on the signed distance function (SDF) of the underlying body and the current garment vertex positions, ReFU predicts the per-vertex offsets that push any interpenetrating vertex to a collision-free configuration while preserving the fine geometric details. We show that ReFU is differentiable with trainable parameters and can be integrated into different network backbones that predict 3D garment deformations. Our experiments show that ReFU significantly reduces the number of collisions between the body and the garment and better preserves geometric details compared to prior methods based on collision loss or post-processing optimization.

Related papers

• An Embeddable Implicit IUVD Representation for Part-based 3D Human Surface Reconstruction [22.349648542401805]
  To reconstruct a 3D human surface from a single image, it is crucial to simultaneously consider human pose, shape, and clothing details. Recent approaches have combined parametric body models (such as SMPL), which capture body pose and shape priors. We propose a novel IUVD-Feedback representation consisting of an IUVD occupancy function and a feedback query algorithm.
  arXiv  Detail & Related papers  (2024-01-30T08:14:04Z)
• NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation [53.25973084799954]
  We present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bi-directional errors. NIKI can learn from both the forward and inverse processes with invertible networks.
  arXiv  Detail & Related papers  (2023-05-15T12:13:24Z)
• Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Search [23.52274863244624]
  Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components. It has been widely used in game engines, physics simulations, and animation. We propose a novel method that addresses the limitations of existing approaches from three perspectives.
  arXiv  Detail & Related papers  (2022-05-05T23:40:15Z)
• Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations [68.0524382279567]
  We present a robust learning algorithm to detect and handle collisions in 3D deforming meshes. Our approach outperforms supervised learning methods and achieves $93.8-98.1%$ accuracy.
  arXiv  Detail & Related papers  (2021-10-08T04:08:31Z)
• DC-GNet: Deep Mesh Relation Capturing Graph Convolution Network for 3D Human Shape Reconstruction [1.290382979353427]
  We propose a Deep Mesh Relation Capturing Graph Convolution Network, DC-GNet, with a shape completion task for 3D human shape reconstruction. Our approach encodes mesh structure from more subtle relations between nodes in a more distant region. Our shape completion module alleviates the performance degradation issue in the outdoor scene.
  arXiv  Detail & Related papers  (2021-08-27T16:43:32Z)
• Unsupervised Geodesic-preserved Generative Adversarial Networks for Unconstrained 3D Pose Transfer [84.04540436494011]
  We present an unsupervised approach to conduct the pose transfer between any arbitrated given 3D meshes. Specifically, a novel Intrinsic-Extrinsic Preserved Generative Adrative Network (IEP-GAN) is presented for both intrinsic (i.e., shape) and extrinsic (i.e., pose) information preservation. Our proposed model produces better results and is substantially more efficient compared to recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-17T09:08:21Z)
• Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On [29.458328272854107]
  We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on. We show that our method is the first to successfully address garment-body contact in unseen body shapes and motions, without compromising realism and detail.
  arXiv  Detail & Related papers  (2021-05-13T17:58:20Z)
• GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss [89.96698250086064]
  We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies. Our network learns to mimic a Physics-Based Simulation (PBS) method while requiring two orders of magnitude less computation time. We validate our framework on four garment types for various body shapes and poses.
  arXiv  Detail & Related papers  (2020-07-20T13:40:15Z)
• Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering [53.16864661460889]
  Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. In this paper, we introduce body segmentation as critical supervision. To improve the reconstruction with part segmentation, we propose a part-level differentiable part that enables part-based models to be supervised by part segmentation.
  arXiv  Detail & Related papers  (2020-03-24T14:25:46Z)

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.