A multi-scale GAN framework with per-level conditioning synthesizes controllable human motion and co-speech gestures from limited data, showing higher in-sample diversity than GANimator.
From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
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abstract
We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction. Given speech audio, we output multiple possibilities of gestural motion for an individual, including face, body, and hands. The key behind our method is in combining the benefits of sample diversity from vector quantization with the high-frequency details obtained through diffusion to generate more dynamic, expressive motion. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures (e.g. sneers and smirks). To facilitate this line of research, we introduce a first-of-its-kind multi-view conversational dataset that allows for photorealistic reconstruction. Experiments show our model generates appropriate and diverse gestures, outperforming both diffusion- and VQ-only methods. Furthermore, our perceptual evaluation highlights the importance of photorealism (vs. meshes) in accurately assessing subtle motion details in conversational gestures. Code and dataset available online.
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cs.CV 1years
2024 1verdicts
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Multi-Resolution Generative Modeling of Human Motion from Limited Data
A multi-scale GAN framework with per-level conditioning synthesizes controllable human motion and co-speech gestures from limited data, showing higher in-sample diversity than GANimator.