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We present a novel framework for generating photorealistic 3D human head and subsequently manipulating and reposing them with remarkable flexibility. The proposed approach constructs an implicit representation of 3D human heads, anchored on a parametric face model. To enhance representational capabilities and encode spatial information, we represent semantic consistent head region by a local tri-plane, modulated by a 3D Gaussian. Additionally, we parameterize these tri-planes in a 2D UV space via a 3DMM, enabling effective utilization of the diffusion model for 3D head avatar generation. Our method facilitates the creation of diverse and realistic 3D human heads with flexible global and fine-grained region-based editing over facial structures, appearance and expressions. Extensive experiments demonstrate the effectiveness of our method.

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LOC3DIFF: Local Diffusion for 3D Human Head Synthesis and Editing

  • 会议论文

  • 发表日期：

    2025年

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  • Yushi Lan,
  • Feitong Tan,
  • Qiangeng Xu,
  • Di Qiu,
  • Kyle Genova,
  • Zeng Huang,
  • Sean Fanello,
  • Rohit Pandey,
  • Thomas Funkhouser,
  • Chen Change Loy,
  • Yinda Zhang

摘要

We present a novel framework for generating photorealistic 3D human head and subsequently manipulating and reposing them with remarkable flexibility. The proposed approach constructs an implicit representation of 3D human heads, anchored on a parametric face model. To enhance representational capabilities and encode spatial information, we represent semantic consistent head region by a local tri-plane, modulated by a 3D Gaussian. Additionally, we parameterize these tri-planes in a 2D UV space via a 3DMM, enabling effective utilization of the diffusion model for 3D head avatar generation. Our method facilitates the creation of diverse and realistic 3D human heads with flexible global and fine-grained region-based editing over facial structures, appearance and expressions. Extensive experiments demonstrate the effectiveness of our method.