Liang Lin

Overcome the scarcity of paired data in speech-preserving facial expression manipulation by personalizing visual-language model prompts with individual visual information and correlating changes in visual and semantic features.

Bridging the gap between human manipulation and robotic control, JoyAI-RA unlocks enhanced cross-embodiment behavior learning through multi-source pretraining.

Speakers expressing the same content with different emotions exhibit surprisingly consistent spatial-temporal correlations in their local facial animations, unlocking a new approach to speech-preserving facial expression manipulation.