  We introduce DoubleField, a novel framework combining the merits of both
surface field and radiance field for high-fidelity human reconstruction and
rendering. Within DoubleField, the surface field and radiance field are
associated together by a shared feature embedding and a surface-guided sampling
strategy. Moreover, a view-to-view transformer is introduced to fuse multi-view
features and learn view-dependent features directly from high-resolution
inputs. With the modeling power of DoubleField and the view-to-view
transformer, our method significantly improves the reconstruction quality of
both geometry and appearance, while supporting direct inference, scene-specific
high-resolution finetuning, and fast rendering. The efficacy of DoubleField is
validated by the quantitative evaluations on several datasets and the
qualitative results in a real-world sparse multi-view system, showing its
superior capability for high-quality human model reconstruction and
photo-realistic free-viewpoint human rendering. Data and source code will be
made public for the research purpose. Please refer to our project page:
http://www.liuyebin.com/dbfield/dbfield.html.
