119 citations · 297 across the 17 of their papers we have counts for
8 papers · 1 filter
MOCHA: Real-Time Motion Characterization via Context Matching
Deok-Kyeong Jang, Yuting Ye, Jungdam Won +1
Transforming neutral, characterless input motions to embody the distinct style of a notable character in real time is highly compelling for character animation. This paper introduc…
DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics
Yifeng Jiang, Jungdam Won, Yuting Ye +1
Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports…
Physics-based Motion Retargeting from Sparse Inputs
Daniele Reda, Jungdam Won, Yuting Ye +2
Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/…
QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors
Sunmin Lee, Sebastian Starke, Yuting Ye +2
Replicating a user's pose from only wearable sensors is important for many AR/VR applications. Most existing methods for motion tracking avoid environment interaction apart from fo…
Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions
Jungnam Park, Moon Seok Park, Jehee Lee +1
We present a novel generative model, called Bidirectional GaitNet, that learns the relationship between human anatomy and its gait. The simulation model of human anatomy is a compr…
Simulation and Retargeting of Complex Multi-Character Interactions
Yunbo Zhang, Deepak Gopinath, Yuting Ye +3
We present a method for reproducing complex multi-character interactions for physically simulated humanoid characters using deep reinforcement learning. Our method learns control p…
ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters
Tianyu Li, Jungdam Won, Alexander Clegg +3
Motion retargeting is a promising approach for generating natural and compelling animations for nonhuman characters. However, it is challenging to translate human movements into se…
PMP: Learning to Physically Interact with Environments using Part-wise Motion Priors
Jinseok Bae, Jungdam Won, Donggeun Lim +2
We present a method to animate a character incorporating multiple part-wise motion priors (PMP). While previous works allow creating realistic articulated motions from reference da…