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University of Illinois Urbana-Champaign
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AniGS transforms static 3D reconstructions into dynamic, immersive environments by seamlessly integrating ambient motion without compromising structural fidelity.
Achieving a 95.8% success rate in resolving self-collisions, PoseShield transforms how we handle human pose estimation under extreme articulations.
A single generalist model outperforms specialized systems, achieving over 35% improvement in real-world robotic task success.
LLMs can scalably annotate motion capture data to produce semantically rich descriptions of bimanual interactions, enabling higher-quality generation of dexterous hand motions.
Humanoids can now perform complex loco-manipulation tasks from egocentric vision and sparse goals, thanks to a unified controller trained without relying on predefined motion references at test time.
