← 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 5Source ↗code ↗Scholar ↗
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 15Source ↗PDF ↗Scholar ↗
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 4Source ↗PDF ↗Scholar ↗
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 14Source ↗PDF ↗Scholar ↗
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 28Source ↗Scholar ↗
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 26Source ↗Scholar ↗