e motion.
Existing video generation models prioritize appearance fidelity, resulting in
unrealistic or physically implausible human movements with poor structural
coherence. Additionally, most existing human video datasets primarily focus on
facial or upper-body motions, or consist of vertically oriented dance videos,
limiting the scope of corresponding generation methods to simple movements. To
overcome these challenges, we propose MoCo, which decouples the process of
human video generation into two compon