Beyond scene geometry and sparse tracking signals, S2Fusion can leverage additional modalities to further enhance the quality and diversity of the generated full-body motions. One potential modality is audio data, which can provide cues about the context or intention of the movements. By incorporating audio signals into the model, S2Fusion can generate motions that are synchronized with speech or sound cues, adding another layer of realism to the animations.
Another modality that can be leveraged is contextual information from the environment. This could include factors such as lighting conditions, temperature, or even emotional cues from the surroundings. By integrating these contextual cues into the model, S2Fusion can generate motions that are more contextually aware and responsive to the environment.
Additionally, incorporating feedback mechanisms or reinforcement learning techniques can enable S2Fusion to adapt and improve its motion generation based on user interactions or preferences. By learning from user feedback, the model can continuously refine and optimize the generated motions to better meet the user's expectations and requirements.