CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network

by Yudi Li1, Min Tang1, Yun Yang1, Ruofeng Tong1, Shuangcai Yang2, Yao Li2, Bailin An2, and Qilong Kou2

1 - Zhejiang University

2 - Tencent

Abstract

We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network. The characters processed in our approach are not limited to humans, and can be other skeletal-based representations of non-human targets such as fish or pets. We use a novel network architecture which consists of skeleton-based and mesh-based residual networks to learn the coarse and wrinkle features as the overall residual from the template cloth mesh. Our network is used to predict the deformation for loose or tight-fitting clothing or dresses. We ensure that the memory footprint of our network is low, and thereby result in reduced storage and computational requirements. In practice, our prediction for a single cloth mesh for the skeleton-based character takes about 7 milliseconds on an NVIDIA GeForce RTX 3090 GPU. Compared with prior methods, our network can generate fine deformation results with details and wrinkles.

Results

To evaluate that our network can process more complex and different characters, we applied our network on non-human characters such as a monster, a dolphin, and a cat. The monster character has a skeleton similar to the human character, while the dolphin and the cat have different skeletons. The dolphin character has no leg joints, while the cat model has four legs without hands. We can also simulate the cloth deformation on these characters. The monster character wears a loose robe, and the dolphin and the cat wear tight-fitting clothes designed for these characters.  

Contents

Paper  (PDF 12.1 MB)

Video (30.9 MB) also at Youtube


Yudi Li, Min Tang, Yun Yang, Ruofeng Tong, Shuangcai Yang, Yao Li, Bailin An, Qilong Kou, CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network, accepted by Computational Visual Media (Proceedings of CVM 2023), 2023.

   @article{ncloth22,
      author = {Li, Yudi and Tang, Min and Yang, Yun and Tong, Ruofeng and Yang, Shuangcai and Li, Yao and An, Bailin and Kou, Qilong},
      title = {{CTSN}: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network},
      journal = {Computational Visual Media (Proceedings of CVM 2023)},
      year = {2023},
  }

 

Related Links

D-Cloth: Skinning-based Cloth Dynamic Prediction with a Three-stage Network

N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks

P-Cloth: Interactive Cloth Simulation on Multi-GPU Systems using Dynamic Matrix Assembly and Pipelined Implicit Integrators

I-Cloth: Incremental Collision Handling for GPU-Based Interactive Cloth Simulation

PSCC: Parallel Self-Collision Culling with Spatial Hashing on GPUs

I-Cloth: API for fast and reliable cloth simulation with CUDA

Efficient BVH-based Collision Detection Scheme with Ordering and Restructuring

MCCD: Multi-Core Collision Detection between Deformable Models using Front-Based Decomposition

Interactive Continuous Collision Detection between Deformable Models using Connectivity-Based Culling

TightCCD: Efficient and Robust Continuous Collision Detection using Tight Error Bounds

Fast and Exact Continuous Collision Detection with Bernstein Sign Classification

A GPU-based Streaming Algorithm for High-Resolution Cloth Simulation

Continuous Penalty Forces

UNC dynamic model benchmark repository

Collision-Streams: Fast GPU-based Collision Detection for Deformable Models

Fast Continuous Collision Detection using Deforming Non-Penetration Filters

Fast Collision Detection for Deformable Models using Representative-Triangles

DeformCD: Collision Detection between Deforming Objects

Self-CCD: Continuous Collision Detection for Deforming Objects

Interactive Collision Detection between Deformable Models using Chromatic Decomposition

Fast Proximity Computation Among Deformable Models using Discrete Voronoi Diagrams

CULLIDE: Interactive Collision Detection between Complex Models using Graphics Hardware

RCULLIDE: Fast and Reliable Collision Culling using Graphics Processors

Quick-CULLIDE: Efficient Inter- and Intra-Object Collision Culling using Graphics Hardware

Collision Detection

UNC GAMMA Group

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant No.: 61972341, Grant No.: 61972342, Grant No.: 61732015, and the Tencent-Zhejiang University joint laboratory.

 


tang_m@zju.edu.cn