Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video
  Diffusion
Animatable head avatar generation typically requires extensive data for
training. To reduce the data requirements, a natural solution is to leverage
existing data-free static avatar generation methods, such as pre-trained
diffusion models with score distillation sampling (SDS), which align avatars
with pseudo ground-truth outputs from the diffusion model. However, directly
distilling 4D avatars from video diffusion often leads to over-smooth results
due to spatial and temporal inconsistencies in the generated video. To address
this issue, we propose Zero-1-to-A, a robust method that synthesizes a spatial
and temporal consistency dataset for 4D avatar reconstruction using the video
diffusion model. Specifically, Zero-1-to-A iteratively constructs video
datasets and optimizes animatable avatars in a progressive manner, ensuring
that avatar quality increases smoothly and consistently throughout the learning
process. This progressive learning involves two stages: (1) Spatial Consistency
Learning fixes expressions and learns from front-to-side views, and (2)
Temporal Consistency Learning fixes views and learns from relaxed to
exaggerated expressions, generating 4D avatars in a simple-to-complex manner.
Extensive experiments demonstrate that Zero-1-to-A improves fidelity, animation
quality, and rendering speed compared to existing diffusion-based methods,
providing a solution for lifelike avatar creation. Code is publicly available
at: https://github.com/ZhenglinZhou/Zero-1-to-A.
67dcff844aa37abf77ae7338
Zhou Zhenglin
claimed_verified
2025-03-21T11:40:15.305Z
6425318d175bd2952281065e
/avatars/37deb6ceb1552dece43a1c8c13c1c871.svg
ZhenglinZhou
user
zhenglin
67dcff844aa37abf77ae7339
Ma Fan
67dcff844aa37abf77ae733a
Fan Hehe
67dcff844aa37abf77ae733b
Chua Tat-Seng
Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video
  Diffusion
Animatable head avatar generation typically requires extensive data for
training. To reduce the data requirements, a natural solution is to leverage
existing data-free static avatar generation methods, such as pre-trained
diffusion models with score distillation sampling (SDS), which align avatars
with pseudo ground-truth outputs from the diffusion model. However, directly
distilling 4D avatars from video diffusion often leads to over-smooth results
due to spatial and temporal inconsistencies in the generated video. To address
this issue, we propose Zero-1-to-A, a robust method that synthesizes a spatial
and temporal consistency dataset for 4D avatar reconstruction using the video
diffusion model. Specifically, Zero-1-to-A iteratively constructs video
datasets and optimizes animatable avatars in a progressive manner, ensuring
that avatar quality increases smoothly and consistently throughout the learning
process. This progressive learning involves two stages: (1) Spatial Consistency
Learning fixes expressions and learns from front-to-side views, and (2)
Temporal Consistency Learning fixes views and learns from relaxed to
exaggerated expressions, generating 4D avatars in a simple-to-complex manner.
Extensive experiments demonstrate that Zero-1-to-A improves fidelity, animation
quality, and rendering speed compared to existing diffusion-based methods,
providing a solution for lifelike avatar creation. Code is publicly available
at: https://github.com/ZhenglinZhou/Zero-1-to-A.
A novel method synthesizes a dataset for 4D avatar reconstruction using video diffusion models to improve fidelity and animation quality.
diffusion models score distillation sampling video diffusion spatial consistency temporal consistency animatable avatars progressive learning