Grounding Foundational Vision Models with 3D Human Poses for Robust Action Recognition
This addresses robust action recognition for embodied agents by combining complementary representations to overcome limitations of RGB-only models.
The paper tackles the problem of action recognition in complex scenes by grounding it in physical space through fusion of contextual world dynamics and explicit human pose data, achieving superior performance on InHARD and UCF-19-Y-OCC benchmarks compared to three baselines, particularly in occlusive scenes.
For embodied agents to effectively understand and interact within the world around them, they require a nuanced comprehension of human actions grounded in physical space. Current action recognition models, often relying on RGB video, learn superficial correlations between patterns and action labels, so they struggle to capture underlying physical interaction dynamics and human poses in complex scenes. We propose a model architecture that grounds action recognition in physical space by fusing two powerful, complementary representations: V-JEPA 2's contextual, predictive world dynamics and CoMotion's explicit, occlusion-tolerant human pose data. Our model is validated on both the InHARD and UCF-19-Y-OCC benchmarks for general action recognition and high-occlusion action recognition, respectively. Our model outperforms three other baselines, especially within complex, occlusive scenes. Our findings emphasize a need for action recognition to be supported by spatial understanding instead of statistical pattern recognition.