To extend S2Fusion for more complex human-object interactions in 3D scenes, several enhancements can be considered. One approach is to incorporate additional sensors or modalities to capture more detailed information about the interactions. For example, depth sensors or RGB-D cameras can provide depth information that can help in understanding the spatial relationships between humans and objects in the scene. By integrating this depth information into the model, S2Fusion can better estimate the interactions and generate more accurate and realistic motions.
Another extension could involve incorporating semantic information about the objects in the scene. By leveraging object recognition algorithms or semantic segmentation techniques, S2Fusion can identify different objects in the environment and adjust the generated motions accordingly. For instance, if a person is interacting with a chair, the model can adapt the motion to simulate sitting or standing up from the chair.
Furthermore, integrating physics-based simulations or constraints into the model can enhance the realism of the generated motions. By incorporating knowledge of physical interactions, such as gravity, friction, or collision detection, S2Fusion can generate more physically plausible movements in complex human-object interactions.