DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis
Authors: name: Yuming Gu, name: Phong Tran, name: Yujian Zheng, name: Hongyi Xu, name: Heyuan Li, name: Adilbek Karmanov, name: Hao Li

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.