SkillFormer: Unified Multi-View Video Understanding for Proficiency
  Estimation
Authors: name: Edoardo Bianchi, name: Antonio Liotta

Assessing human skill levels in complex activities is a challenging problem
with applications in sports, rehabilitation, and training. In this work, we
present SkillFormer, a parameter-efficient architecture for unified multi-view
proficiency estimation from egocentric and exocentric videos. Building on the
TimeSformer backbone, SkillFormer introduces a CrossViewFusion module that
fuses view-specific features using multi-head cross-attention, learnable
gating, and adaptive self-calibration. We leverage Low-Rank Adaptation to
fine-tune only a small subset of parameters, significantly reducing training
costs. In fact, when evaluated on the EgoExo4D dataset, SkillFormer achieves
state-of-the-art accuracy in multi-view settings while demonstrating remarkable
computational efficiency, using 4.5x fewer parameters and requiring 3.75x fewer
training epochs than prior baselines. It excels in multiple structured tasks,
confirming the value of multi-view integration for fine-grained skill
assessment.