DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis
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.
67e358f8f16653f6dc99f162
Yuming Gu
claimed_verified
2025-03-26T20:44:41.513Z
64d69b22bcab729cb4b0a6ee
/avatars/17604cb8577cb769e07dd40fc22a1cd5.svg
Yuming Gu
user
gym890
67e358f8f16653f6dc99f163
Phong Tran
67e358f8f16653f6dc99f164
Yujian Zheng
67e358f8f16653f6dc99f165
Hongyi Xu
67e358f8f16653f6dc99f166
Heyuan Li
67e358f8f16653f6dc99f167
Adilbek Karmanov
67e358f8f16653f6dc99f168
Hao Li
DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis
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.
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.
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