1 research outputs found
TADA! Text to Animatable Digital Avatars
We introduce TADA, a simple-yet-effective approach that takes textual
descriptions and produces expressive 3D avatars with high-quality geometry and
lifelike textures, that can be animated and rendered with traditional graphics
pipelines. Existing text-based character generation methods are limited in
terms of geometry and texture quality, and cannot be realistically animated due
to inconsistent alignment between the geometry and the texture, particularly in
the face region. To overcome these limitations, TADA leverages the synergy of a
2D diffusion model and an animatable parametric body model. Specifically, we
derive an optimizable high-resolution body model from SMPL-X with 3D
displacements and a texture map, and use hierarchical rendering with score
distillation sampling (SDS) to create high-quality, detailed, holistic 3D
avatars from text. To ensure alignment between the geometry and texture, we
render normals and RGB images of the generated character and exploit their
latent embeddings in the SDS training process. We further introduce various
expression parameters to deform the generated character during training,
ensuring that the semantics of our generated character remain consistent with
the original SMPL-X model, resulting in an animatable character. Comprehensive
evaluations demonstrate that TADA significantly surpasses existing approaches
on both qualitative and quantitative measures. TADA enables creation of
large-scale digital character assets that are ready for animation and
rendering, while also being easily editable through natural language. The code
will be public for research purposes