Jungdam Won
Papers
4
Total Citations
31
H-Index
3
About
Jungdam Won is a leading researcher in computer animation and robotics, specializing in motion retargeting and locomotion control. His work bridges the gap between human motion and nonhuman characters, as well as between simulation and real-world robot deployment. In his highly cited paper "ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters" (16 citations, 2023), Won introduced a novel adversarial learning framework that enables semantically consistent translation of human movements onto creatures with vastly different body structures—a breakthrough for game and film animation. His "FastMimic" series (over 15 combined citations) tackles the challenge of agile, diverse quadrupedal locomotion by combining model-based control with motion imitation, allowing robots to perform a wide repertoire of behaviors—from slow walking to rapid turning—without task-specific engineering. This work has significant implications for legged robots operating in human environments. Won’s research is notable for its practical impact on both the animation industry and robotics, demonstrating how motion imitation can generalize across morphologies and platforms.
Research Focus
Key Achievements
Top Papers
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