5 citations · 13 across the 9 of their papers we have counts for
9 papers
PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers
Michael Xu, Yi Shi, KangKang Yin +1
Humans excel in navigating diverse, complex environments with agile motor skills, exemplified by parkour practitioners performing dynamic maneuvers, such as climbing up walls and j…
AAMDM: Accelerated Auto-regressive Motion Diffusion Model
Tianyu Li, Calvin Qiao, Guanqiao Ren +2
Interactive motion synthesis is essential in creating immersive experiences in entertainment applications, such as video games and virtual reality. However, generating animations t…
Learning to Use Chopsticks in Diverse Gripping Styles
Zeshi Yang, KangKang Yin, Libin Liu
Learning dexterous manipulation skills is a long-standing challenge in computer graphics and robotics, especially when the task involves complex and delicate interactions between t…
Robust Visual Teach and Repeat for UGVs Using 3D Semantic Maps
Mohammad Mahdavian, KangKang Yin, Mo Chen
We propose a Visual Teach and Repeat (VTR) algorithm using semantic landmarks extracted from environmental objects for ground robots with fixed mount monocular cameras. The propose…
Discovering Diverse Athletic Jumping Strategies
Zhiqi Yin, Zeshi Yang, Michiel van de Panne +1
We present a framework that enables the discovery of diverse and natural-looking motion strategies for athletic skills such as the high jump. The strategies are realized as control…
Learning and Exploring Motor Skills with Spacetime Bounds
Li-Ke Ma, Zeshi Yang, Xin Tong +2
Equipping characters with diverse motor skills is the current bottleneck of physics-based character animation. We propose a Deep Reinforcement Learning (DRL) framework that enables…
Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features
Zeshi Yang, Kangkang Yin
Recently skeleton-based action recognition has made signif-icant progresses in the computer vision community. Most state-of-the-art algorithms are based on Graph Convolutional Netw…
Hierarchical Action Classification with Network Pruning
Mahdi Davoodikakhki, KangKang Yin
Research on human action classification has made significant progresses in the past few years. Most deep learning methods focus on improving performance by adding more network comp…
Towards Robust Direction Invariance in Character Animation
Li-Ke Ma, Zeshi Yang, Baining Guo +1
In character animation, direction invariance is a desirable property. That is, a pose facing north and the same pose facing south are considered the same; a character that can walk…