ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos

ExpertEdit addresses a high-value niche: personalized athletic and rehabilitative feedback. Its core innovation lies in removing the requirement for paired 'amateur-expert' data, which is historically a significant bottleneck in motion transfer tasks. However, its defensibility is currently very low (score 3) because it is a nascent research project with negligible traction (0 stars, 2 forks). The technical moat resides in the specific 'skill-aware' loss functions or architectural constraints used to maintain identity while improving form, but these are likely to be eclipsed by general-purpose video-to-video diffusion models from frontier labs (OpenAI's Sora, Google's Veo) or specialized startups like Runway and Luma. Companies like Move.ai or DeepMotion already dominate the professional motion capture space and could integrate this 'skill refinement' logic as a post-processing feature. The displacement horizon is short (6 months) because the field of human motion synthesis is moving rapidly toward high-fidelity, zero-shot video editing where 'make this look more professional' becomes a natural language prompt rather than a bespoke architectural problem.