SkillMoV: Mixture-of-View Routing with Prototype-Conditioned Gating for Unified Multi-View Proficiency Estimation

2026-06-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

SkillMoV is a unified, parameter-efficient framework designed for estimating human proficiency from synchronized multi-view video across various scenarios. It addresses challenges in adapting to heterogeneous camera viewpoints and activity domains by introducing a Mixture-of-View Projector (MoVP). MoVP comprises a Mixture-of-View soft router with twelve expert MLPs for view-dependent preferences, cross-view attention for camera alignment, learnable prototype anchoring, and a prototype-conditioned gated projection for skill embedding. Evaluated on EgoExo4D across six skill domains and three view configurations (Ego, Exos, Ego+Exos), SkillMoV achieved 50.17% overall accuracy in the Exos setting, outperforming the strongest reported method by 3.57 percentage points. In the Ego+Exos configuration, it reached 47.63%. Ablation studies confirmed significant contributions from MoV routing (+6.61 pp), cross-view attention (+4.92 pp), and prototype anchoring (+4.07 pp). Furthermore, LoRA adaptation allows SkillMoV to train only 23.32% of its parameters with minimal overhead.

Key takeaway

For Machine Learning Engineers developing automated skill assessment systems from multi-view video, SkillMoV provides a robust and parameter-efficient solution. You should consider its Mixture-of-View Projector architecture, which achieved 50.17% accuracy in the Exos setting, for handling heterogeneous camera viewpoints. Its LoRA adaptation, training only 23.32% of parameters, offers significant efficiency gains, reducing overhead while maintaining high performance across diverse skill domains.

Key insights

SkillMoV unifies multi-view human proficiency estimation using a Mixture-of-View Projector with prototype-conditioned gating.

Principles

• View-dependent expert routing improves multi-view adaptation.
• Cross-view attention aligns synchronized camera features.
• Prototype anchoring conditions representations on class references.

Method

SkillMoV's MoVP employs a 12-expert MLP router, cross-view attention, learnable prototype anchoring, and gated projection to generate skill embeddings from multi-view video.

In practice

• Apply MoVP for robust multi-camera skill assessment.
• Use LoRA for efficient model adaptation.
• Integrate prototype anchoring for class-level conditioning.

Topics

• Multi-View Learning
• Proficiency Estimation
• Mixture-of-Experts
• Video Analysis
• Skill Assessment
• LoRA Adaptation
• EgoExo4D

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.