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Found 2 papers, 1 papers with code
AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech Gesture Synthesis
By learning the mapping of a latent space representation as opposed to directly mapping it to a vector representation, this framework facilitates the generation of highly realistic and expressive gestures that closely replicate human movement and behavior, while simultaneously avoiding artifacts in the generation process.
Ranked #1 on
Gesture Generation
on TED Gesture Dataset
Addressing Data Scarcity in Multimodal User State Recognition by Combining Semi-Supervised and Supervised Learning
Detecting mental states of human users is crucial for the development of cooperative and intelligent robots, as it enables the robot to understand the user's intentions and desires.
