Moving Beyond More Views: Redundancy-Aware Ego–Exo Fusion for Proficiency Estimation

EgoExo proficiency estimation aims to assess action qual-ity by integrating fine-grained motion cues from egocentric (1st-person)views with spatial context from multiple exocentric (3rd-person) views.Simply adding more exocentric views degrades EgoExo performance, asredundant or noisy perspectives dilute useful motion cues. Our anal-ysis identifies two key causes: (1) Multiview redundancy — Fromthe data perspective, certain views provide limited or noisy informa-tion, diluting discriminative cues; (2) Overfitting — From the featureperspective, conventional fusion increases representational complexity,causing the model to memorise view-specific patterns rather than learngeneralisable representations. To address these issues, we propose twocomplementary modules. AdaMVS adaptively identifies and fuses themost informative view tokens under weak supervision from the data per-spective, while VIB-GB combines Gradient Blending and VariationalInformation Bottleneck regularisation from the feature perspective tocompress redundant signals and suppress overfitting during training.Experiments on EgoExo-4D and EgoExo-Fitness demonstrate that ourmethod learns both which view to look at and how to fuse them,achieving new state-of-the-art results. Our source code is available athttps://github.com/dx199771/AdaMVS