title: Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video
    Diffusion
summary: 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.
paper_authors._id: 67dcff844aa37abf77ae7338
paper_authors.name: Zhou Zhenglin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-21T11:40:15.305Z
paper_authors._id: 6425318d175bd2952281065e
paper_authors.avatarUrl: /avatars/37deb6ceb1552dece43a1c8c13c1c871.svg
paper_authors.fullname: ZhenglinZhou
paper_authors.type: user
paper_authors.user: zhenglin
paper_authors._id: 67dcff844aa37abf77ae7339
paper_authors.name: Ma Fan
paper_authors._id: 67dcff844aa37abf77ae733a
paper_authors.name: Fan Hehe
paper_authors._id: 67dcff844aa37abf77ae733b
paper_authors.name: Chua Tat-Seng
paper_title: Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video
    Diffusion
paper_summary: 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.
paper_ai_summary: A novel method synthesizes a dataset for 4D avatar reconstruction using video diffusion models to improve fidelity and animation quality.
paper_ai_keywords: diffusion models score distillation sampling video diffusion spatial consistency temporal consistency animatable avatars progressive learning