42 citations · 47 across the 5 of their papers we have counts for
5 papers
The Last Mile to Production Readiness: Physics-Based Motion Refinement for Video-Based Capture
Tianxin Tao, Han Liu, Hung Yu Ling
High-quality motion data underpins games, film, XR, and robotics. Vision-based motion capture tools have made significant progress, offering accessible and visually convincing resu…
Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels
Tianxin Tao, Daniele Reda, Michiel van de Panne
Vision Transformers (ViT) have recently demonstrated the significant potential of transformer architectures for computer vision. To what extent can image-based deep reinforcement l…
Learning to Get Up
Tianxin Tao, Matthew Wilson, Ruiyu Gou +1
Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not re…
Style-ERD: Responsive and Coherent Online Motion Style Transfer
Tianxin Tao, Xiaohang Zhan, Zhongquan Chen +1
Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in…
Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning
Daniele Reda, Tianxin Tao, Michiel van de Panne
Learning to locomote is one of the most common tasks in physics-based animation and deep reinforcement learning (RL). A learned policy is the product of the problem to be solved, a…