From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
- URL: http://arxiv.org/abs/2401.01885v1
- Date: Wed, 3 Jan 2024 18:55:16 GMT
- Title: From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
- Authors: Evonne Ng, Javier Romero, Timur Bagautdinov, Shaojie Bai, Trevor
Darrell, Angjoo Kanazawa, Alexander Richard
- Abstract summary: Given speech audio, we output multiple possibilities of gestural motion for an individual, including face, body, and hands.
We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures.
Experiments show our model generates appropriate and diverse gestures, outperforming both diffusion- and VQ-only methods.
- Score: 107.88375243135579
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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