119 citations · 297 across the 17 of their papers we have counts for
18 papers
InstantMimic: A High Performance System for Learning Physics-based Skills in Seconds
Ikjun Choi, Geonho Leem, Jungdam Won
Physics-based character control is a long-standing challenge in computer graphics and robotics, requiring policies that satisfy complex dynamics while producing realistic motion. R…
MOCHI: Motion Enhancement of Collaborative Human-object Interactions
Jiye Lee, Yonghun Choi, Jungdam Won
Collaborative human-object interaction shows dynamic and complex movements that require mutual anticipation and continuous adjustment between participants and the shared object. Mo…
Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation
Geonho Leem, Jaedong Lee, Jehee Lee +2
Exoskeletons show great promise for enhancing mobility, but providing appropriate assistance remains challenging due to the complexity of human adaptation to external forces. Curre…
Versatile Physics-based Character Control with Hybrid Latent Representation
Jinseok Bae, Jungdam Won, Donggeun Lim +2
We present a versatile latent representation that enables physically simulated character to efficiently utilize motion priors. To build a powerful motion embedding that is shared a…
Strategy and Skill Learning for Physics-based Table Tennis Animation
Jiashun Wang, Jessica Hodgins, Jungdam Won
Recent advancements in physics-based character animation leverage deep learning to generate agile and natural motion, enabling characters to execute movements such as backflips, bo…
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…
Leveraging Demonstrations with Latent Space Priors
Jonas Gehring, Deepak Gopinath, Jungdam Won +3
Demonstrations provide insight into relevant state or action space regions, bearing great potential to boost the efficiency and practicality of reinforcement learning agents. In th…
QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars
Alexander Winkler, Jungdam Won, Yuting Ye
Real-time tracking of human body motion is crucial for interactive and immersive experiences in AR/VR. However, very limited sensor data about the body is available from standalone…
Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation
Yifeng Jiang, Yuting Ye, Deepak Gopinath +3
Real-time human motion reconstruction from a sparse set of (e.g. six) wearable IMUs provides a non-intrusive and economic approach to motion capture. Without the ability to acquire…
Conditional Motion In-betweening
Jihoon Kim, Taehyun Byun, Seungyoun Shin +2
Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such a…
FastMimic: Model-based Motion Imitation for Agile, Diverse and Generalizable Quadrupedal Locomotion
Tianyu Li, Jungdam Won, Sehoon Ha +1
Robots operating in human environments need various skills, like slow and fast walking, turning, side-stepping, and many more. However, building robot controllers that can exhibit…