Object Concepts Emerge from Motion
The framework learns object-centric visual representations from raw video by using motion boundaries as pseudo-instance supervision instead of human labels or camera calibration. It uses a two-cycle training process involving optical flow and self-training to generate instance masks. This method successfully teaches image encoders to capture object identity and coherence, leading to superior performance in tasks like depth estimation and 3D object detection.