AeroGen: Ground-to-Air Generalization for Action Recognition
Ketul Shah, Anshul Shah, Arun Reddy, Aniket Roy, Celso M. de Melo, Rama Chellappa · 2025
We address the problem of action recognition from aerial views using only ground-based videos for training. Due to the viewpoint-induced domain shift, models trained solely on ground videos exhibit significant performance degradation when naively applied to aerial videos. To mitigate this performance gap, we introduce a domain generalization technique that addresses the viewpoint-induced domain shift. Our method uses available real ground videos to generate additional synthetic training data from both ground and air viewpoints, for improving generalization to aerial video-based action recognition. Specifically, we perform 3D human mesh estimation from the ground videos, then render synthetic videos from alternate viewpoints with additional appearance randomizations. To further align the ground-air and syntheticreal domains, we propose a Dual Domain Alignment loss by enforcing consistency in predictions between the original ground videos and augmented videos from each domain. In order to facilitate research on the problem of ground-to-air generalization for human action recognition, we also create a benchmark by combining parts of the NTU-60, UAV-Human and NEC-DRONE datasets. We demonstrate the effectiveness of our approach on these new benchmarks along with the existing RoCoG-Ground $\rightarrow$ RoCoG-Air benchmark, and also perform extensive ablations.