From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
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.
659620bab6b4c4e5049410e8
Evonne Ng
659620bab6b4c4e5049410e9
Javier Romero
659620bab6b4c4e5049410ea
Timur Bagautdinov
659620bab6b4c4e5049410eb
Shaojie Bai
659620bab6b4c4e5049410ec
Trevor Darrell
659620bab6b4c4e5049410ed
Angjoo Kanazawa
659620bab6b4c4e5049410ee
Alexander Richard
From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
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.
A framework combines vector quantization and diffusion models to generate diverse, dynamic, and photorealistic avatars responding to speech in a conversational setting using a novel multi-view dataset.
vector quantization diffusion photorealistic avatars gestural motion face body hands multi-view conversational dataset perceptual evaluation