To extend the RotAvat approach for dynamic camera scenarios, several modifications can be implemented to accommodate the variability in camera position and orientation. First, the auto-calibration step could be enhanced to continuously estimate the camera parameters in real-time as the scene evolves. This could involve integrating a tracking mechanism that utilizes features from the environment or the moving subjects to maintain an accurate understanding of the camera's position and orientation.
Additionally, the RotAvat framework could incorporate a temporal component that leverages sequential frames to predict the camera's motion trajectory. By analyzing the motion patterns of the camera, the system could dynamically adjust the alignment of the 3D meshes in relation to the changing perspective. This would require the development of algorithms capable of estimating the 3D transformations needed to maintain the alignment of the meshes with respect to the ground plane, even as the camera moves.
Furthermore, incorporating depth information from stereo cameras or depth sensors could significantly enhance the robustness of the RotAvat approach in dynamic environments. This additional data would allow for more accurate 3D reconstructions and better handling of occlusions and perspective distortions that arise from camera movement.