Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and Editing

作者：Yushi Lan, Feitong Tan, Di Qiu, Qiangeng Xu, Kyle Genova, Zeng Huang, S. Fanello, Rohit Pandey, T. Funkhouser, Chen Change Loy, Yinda Zhang · 发表于：arXiv.org · 年份：2023 · DOI：10.48550/arXiv.2312.03763 · 被引用次数：30 · 研究领域：Computer Science

We present a novel framework for generating photorealistic 3D human head and subsequently manipulating and reposing them with remarkable flexibility. The proposed approach leverages an implicit function representation of 3D human heads, employing 3D Gaussians anchored on a parametric face model. To enhance representational capabilities and encode spatial information, we embed a lightweight tri-plane payload within each Gaussian rather than directly storing color and opacity. Additionally, we parameterize the Gaussians 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 fine-grained editing over facial features and expressions. Extensive experiments demonstrate the effectiveness of our method.