← Search

Qingyang Tan

7 accepted papers

2026

Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining

CVPR 2026

High-quality 3D avatar modeling faces a critical trade-off between fidelity and generalization. On the one hand, multi-view studio data enables high-fidelity modeling of humans with precise control over expressions and poses, but it struggles to generalize to real-world data due to limited scale and

Cited by 0SourcecodeScholar
2022

A Repulsive Force Unit for Garment Collision Handling in Neural Networks

ECCV 2022poster

"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).…

Cited by 15SourcePDFScholar
2022

N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations

ICML 2022spotlight

We present a robust learning algorithm to detect and handle collisions in 3D deforming meshes. We first train a neural network to detect collisions and then use a numerical optimization algorithm to resolve penetrations guided by the network. Our learned collision handler can resolve collisions for…

Cited by 4SourcePDFScholar
2021

LCollision: Fast Generation of Collision-Free Human Poses using Learned Non-Penetration Constraints

AAAI 2021technical

We present LCollision, a learning-based method that synthesizes collision-free 3D human poses. At the crux of our approach is a novel deep architecture that simultaneously decodes new human poses from the latent space and predicts colliding body parts. These two components of our architecture are us…

Cited by 14SourcePDFScholar
2020

DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance

IROS 2020poster

We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL). Our approach uses local and global information for each robot from motion information maps. We use a three-layer CNN that takes these maps as input to generate a…

Cited by 28SourceScholar
2020

Realtime Simulation of Thin-Shell Deformable Materials Using CNN-Based Mesh Embedding

RA-L 2020

We address the problem of accelerating thin-shell deformable object simulations by dimension reduction. We present a new algorithm to embed a high-dimensional configuration space of deformable objects in a low-dimensional feature space, where the configurations of objects and feature points have app

Cited by 26SourceScholar