title: DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis
summary: Generating high-quality 360-degree views of human heads from single-view
  images is essential for enabling accessible immersive telepresence applications
  and scalable personalized content creation. While cutting-edge methods for full
  head generation are limited to modeling realistic human heads, the latest
  diffusion-based approaches for style-omniscient head synthesis can produce only
  frontal views and struggle with view consistency, preventing their conversion
  into true 3D models for rendering from arbitrary angles. We introduce a novel
  approach that generates fully consistent 360-degree head views, accommodating
  human, stylized, and anthropomorphic forms, including accessories like glasses
  and hats. Our method builds on the DiffPortrait3D framework, incorporating a
  custom ControlNet for back-of-head detail generation and a dual appearance
  module to ensure global front-back consistency. By training on continuous view
  sequences and integrating a back reference image, our approach achieves robust,
  locally continuous view synthesis. Our model can be used to produce
  high-quality neural radiance fields (NeRFs) for real-time, free-viewpoint
  rendering, outperforming state-of-the-art methods in object synthesis and
  360-degree head generation for very challenging input portraits.
paper_authors._id: 67e358f8f16653f6dc99f162
paper_authors.name: Yuming Gu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-26T20:44:41.513Z
paper_authors._id: 64d69b22bcab729cb4b0a6ee
paper_authors.avatarUrl: /avatars/17604cb8577cb769e07dd40fc22a1cd5.svg
paper_authors.fullname: Yuming Gu
paper_authors.type: user
paper_authors.user: gym890
paper_authors._id: 67e358f8f16653f6dc99f163
paper_authors.name: Phong Tran
paper_authors._id: 67e358f8f16653f6dc99f164
paper_authors.name: Yujian Zheng
paper_authors._id: 67e358f8f16653f6dc99f165
paper_authors.name: Hongyi Xu
paper_authors._id: 67e358f8f16653f6dc99f166
paper_authors.name: Heyuan Li
paper_authors._id: 67e358f8f16653f6dc99f167
paper_authors.name: Adilbek Karmanov
paper_authors._id: 67e358f8f16653f6dc99f168
paper_authors.name: Hao Li
paper_title: DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis
paper_summary: Generating high-quality 360-degree views of human heads from single-view
  images is essential for enabling accessible immersive telepresence applications
  and scalable personalized content creation. While cutting-edge methods for full
  head generation are limited to modeling realistic human heads, the latest
  diffusion-based approaches for style-omniscient head synthesis can produce only
  frontal views and struggle with view consistency, preventing their conversion
  into true 3D models for rendering from arbitrary angles. We introduce a novel
  approach that generates fully consistent 360-degree head views, accommodating
  human, stylized, and anthropomorphic forms, including accessories like glasses
  and hats. Our method builds on the DiffPortrait3D framework, incorporating a
  custom ControlNet for back-of-head detail generation and a dual appearance
  module to ensure global front-back consistency. By training on continuous view
  sequences and integrating a back reference image, our approach achieves robust,
  locally continuous view synthesis. Our model can be used to produce
  high-quality neural radiance fields (NeRFs) for real-time, free-viewpoint
  rendering, outperforming state-of-the-art methods in object synthesis and
  360-degree head generation for very challenging input portraits.
paper_ai_summary: A novel approach generates consistent 360-degree views of human heads using a modified DiffPortrait3D framework, incorporating a ControlNet for back-of-head details, to produce high-quality NeRFs for real-time rendering.
paper_ai_keywords: diffusion-based approaches DiffPortrait3D ControlNet dual appearance module continuous view sequences back reference image neural radiance fields NeRFs real-time free-viewpoint rendering object synthesis 360-degree head generation