AI RESEARCH
MuSteerNet: Human Reaction Generation from Videos via Observation-Reaction Mutual Steering
arXiv CS.CV
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ArXi:2603.20187v1 Announce Type: new Video-driven human reaction generation aims to synthesize 3D human motions that directly react to observed video sequences, which is crucial for building human-like interactive AI systems. However, existing methods often fail to effectively leverage video inputs to steer human reaction synthesis, resulting in reaction motions that are mismatched with the content of video sequences. We reveal that this limitation arises from a severe relational distortion between visual observations and reaction types.