HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting
Zhenglin Zhou Fan Ma Hehe Fan Yi Yang
Creating digital avatars from textual prompts has long been a desirable yet
challenging task. Despite the promising outcomes obtained through 2D diffusion
priors in recent works, current methods face challenges in achieving
high-quality and animated avatars effectively. In this paper, we present
$\textbf{HeadStudio}$, a novel framework that utilizes 3D Gaussian splatting to
generate realistic and animated avatars from text prompts. Our method drives 3D
Gaussians semantically to create a flexible and achievable appearance through
the intermediate FLAME representation. Specifically, we incorporate the FLAME
into both 3D representation and score distillation: 1) FLAME-based 3D Gaussian
splatting, driving 3D Gaussian points by rigging each point to a FLAME mesh. 2)
FLAME-based score distillation sampling, utilizing FLAME-based fine-grained
control signal to guide score distillation from the text prompt. Extensive
experiments demonstrate the efficacy of HeadStudio in generating animatable
avatars from textual prompts, exhibiting visually appealing appearances. The
avatars are capable of rendering high-quality real-time ($\geq 40$ fps) novel
views at a resolution of 1024. They can be smoothly controlled by real-world
speech and video. We hope that HeadStudio can advance digital avatar creation
and that the present method can widely be applied across various domains.