title: Text-Guided 3D Face Synthesis -- From Generation to Editing
summary: Text-guided 3D face synthesis has achieved remarkable results by leveraging
  text-to-image (T2I) diffusion models. However, most existing works focus solely
  on the direct generation, ignoring the editing, restricting them from
  synthesizing customized 3D faces through iterative adjustments. In this paper,
  we propose a unified text-guided framework from face generation to editing. In
  the generation stage, we propose a geometry-texture decoupled generation to
  mitigate the loss of geometric details caused by coupling. Besides, decoupling
  enables us to utilize the generated geometry as a condition for texture
  generation, yielding highly geometry-texture aligned results. We further employ
  a fine-tuned texture diffusion model to enhance texture quality in both RGB and
  YUV space. In the editing stage, we first employ a pre-trained diffusion model
  to update facial geometry or texture based on the texts. To enable sequential
  editing, we introduce a UV domain consistency preservation regularization,
  preventing unintentional changes to irrelevant facial attributes. Besides, we
  propose a self-guided consistency weight strategy to improve editing efficacy
  while preserving consistency. Through comprehensive experiments, we showcase
  our method's superiority in face synthesis. Project page:
  https://faceg2e.github.io/.
paper_authors._id: 656d3cccefd0eea7c5cd3c60
paper_authors.name: Yunjie Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-04T09:35:56.117Z
paper_authors._id: 6461d6135e410d3ff8ec3e25
paper_authors.avatarUrl: /avatars/0fd09e1cc1b75751dc7f22e9c166d8b5.svg
paper_authors.fullname: yunjie wu
paper_authors.type: user
paper_authors.user: jiejaingwu
paper_authors._id: 656d3cccefd0eea7c5cd3c61
paper_authors.name: Yapeng Meng
paper_authors._id: 656d3cccefd0eea7c5cd3c62
paper_authors.name: Zhipeng Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T10:47:04.323Z
paper_authors._id: 63427b63e2647466b4252c03
paper_authors.avatarUrl: /avatars/1a9e069c56852fa976578d9538159e92.svg
paper_authors.fullname: hu zhipeng
paper_authors.type: user
paper_authors.user: hufuzhipeng
paper_authors._id: 656d3cccefd0eea7c5cd3c63
paper_authors.name: Lincheng Li
paper_authors._id: 656d3cccefd0eea7c5cd3c64
paper_authors.name: Haoqian Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T10:47:12.886Z
paper_authors._id: 64dc932a968fb479b20dffdf
paper_authors.avatarUrl: /avatars/1b7a574d6f57433509f56b5bf2b66644.svg
paper_authors.fullname: Haoqian Wu
paper_authors.type: user
paper_authors.user: woolseyyy
paper_authors._id: 656d3cccefd0eea7c5cd3c65
paper_authors.name: Kun Zhou
paper_authors._id: 656d3cccefd0eea7c5cd3c66
paper_authors.name: Weiwei Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T10:49:29.548Z
paper_authors._id: 64d461bce1830780115da214
paper_authors.avatarUrl: /avatars/5864b19347911504e84af8c27186bf9d.svg
paper_authors.fullname: Weiwei Xu
paper_authors.type: user
paper_authors.user: xuww
paper_authors._id: 656d3cccefd0eea7c5cd3c67
paper_authors.name: Xin Yu
paper_title: Text-Guided 3D Face Synthesis -- From Generation to Editing
paper_summary: Text-guided 3D face synthesis has achieved remarkable results by leveraging
  text-to-image (T2I) diffusion models. However, most existing works focus solely
  on the direct generation, ignoring the editing, restricting them from
  synthesizing customized 3D faces through iterative adjustments. In this paper,
  we propose a unified text-guided framework from face generation to editing. In
  the generation stage, we propose a geometry-texture decoupled generation to
  mitigate the loss of geometric details caused by coupling. Besides, decoupling
  enables us to utilize the generated geometry as a condition for texture
  generation, yielding highly geometry-texture aligned results. We further employ
  a fine-tuned texture diffusion model to enhance texture quality in both RGB and
  YUV space. In the editing stage, we first employ a pre-trained diffusion model
  to update facial geometry or texture based on the texts. To enable sequential
  editing, we introduce a UV domain consistency preservation regularization,
  preventing unintentional changes to irrelevant facial attributes. Besides, we
  propose a self-guided consistency weight strategy to improve editing efficacy
  while preserving consistency. Through comprehensive experiments, we showcase
  our method's superiority in face synthesis. Project page:
  https://faceg2e.github.io/.
paper_ai_summary: A text-guided framework for 3D face synthesis and editing uses decoupled geometry-texture generation and fine-tuned texture diffusion to achieve high quality and consistent results.
paper_ai_keywords: diffusion models text-to-image (T2I) diffusion models geometry-texture decoupled generation UV domain consistency preservation regularization self-guided consistency weight strategy texture diffusion model