SkillMoV: Mixture-of-View Routing with Prototype-Conditioned Gating for Unified Multi-View Proficiency Estimation
arXiv:2606.17615v1 Announce Type: cross Abstract: Estimating human proficiency from video is a key challenge for automated skill assessment, with applications in sports coaching, music pedagogy, surgical training, and workplace learning. Existing approaches often focus on individual scenarios or rely on shared multi-view aggregation, limiting their ability to adapt to heterogeneous camera viewpoints and activity domains. We introduce SkillMoV, a unified, parameter-efficient framework for multi-scenario proficiency estimation from synchronized multi-view video. At its core, SkillMoV introduces
The continuous advancements in AI and computer vision, coupled with the increasing availability of multi-view video data, make unified proficiency estimation a timely area of research.
This development can significantly enhance objective skill assessment across various fields, leading to improved training methodologies and performance evaluation.
Current fragmented approaches to skill assessment are replaced by a more adaptable and efficient multi-scenario framework, broadening the scope and accuracy of automated evaluation.
- · Sports coaching
- · Surgical training
- · Workplace learning platforms
- · Computer vision researchers
- · Subjective manual assessment methods
- · Single-view AI assessment systems
Automated skill assessment becomes more robust and widely applicable across diverse domains using multi-view video input.
Improved training outcomes and accelerated skill acquisition in areas ranging from physical performance to complex technical tasks.
The integration of such AI systems into everyday learning and professional development could lead to data-driven, personalized education at scale.
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Read at arXiv cs.AI