DreamHuman: Animatable 3D Avatars from Text
Nikos Kolotouros Thiemo Alldieck Andrei Zanfir Eduard Gabriel Bazavan Mihai Fieraru Cristian Sminchisescu
We present DreamHuman, a method to generate realistic animatable 3D human
avatar models solely from textual descriptions. Recent text-to-3D methods have
made considerable strides in generation, but are still lacking in important
aspects. Control and often spatial resolution remain limited, existing methods
produce fixed rather than animated 3D human models, and anthropometric
consistency for complex structures like people remains a challenge. DreamHuman
connects large text-to-image synthesis models, neural radiance fields, and
statistical human body models in a novel modeling and optimization framework.
This makes it possible to generate dynamic 3D human avatars with high-quality
textures and learned, instance-specific, surface deformations. We demonstrate
that our method is capable to generate a wide variety of animatable, realistic
3D human models from text. Our 3D models have diverse appearance, clothing,
skin tones and body shapes, and significantly outperform both generic
text-to-3D approaches and previous text-based 3D avatar generators in visual
fidelity. For more results and animations please check our website at
https://dream-human.github.io.