SAM integration into MIMO

[johndpope]

Segment Anything (SAM) could be very helpful for improving the MIMO framework in several ways:

Enhanced spatial decomposition: SAM's ability to segment any object in an image or video could significantly improve the spatial decomposition process described in Section 2.1 of the paper. Instead of relying solely on depth estimation and human detection, SAM could provide more accurate and detailed segmentation masks for:

Human layer: More precise human segmentation, including fine details like hair and clothing. Occlusion layer: Better detection and segmentation of objects that may occlude the human. Scene layer: Improved separation of background elements.

Improved object interaction handling: SAM's ability to segment arbitrary objects could help in better modeling human-object interactions. This would enhance the "applicability to interactive real-world scenes" that MIMO aims to achieve. Refinement of masklets: The paper mentions using video tracking to propagate masks across frames. SAM could be used to refine these masklets frame-by-frame, potentially improving temporal consistency. Data augmentation: SAM could be used to generate additional training data by creating new combinations of segmented humans, objects, and backgrounds. Evaluation and quality control: SAM could be used to evaluate the quality of the synthesized videos by comparing the segmentation of generated videos with the original inputs. Interactive editing: While not directly mentioned in the paper, SAM's interactive segmentation capabilities could allow for user-guided refinement of the spatial decomposition, enabling more precise control over the synthesis process. Handling complex scenes: For scenes with multiple people or complex object arrangements, SAM could provide a more robust initial segmentation, potentially expanding MIMO's capabilities to handle more diverse and challenging scenarios.

[johndpope]

replace logic with SAM2VideoPredictor