  4D reconstruction and rendering of human activities is critical for immersive
VR/AR experience.Recent advances still fail to recover fine geometry and
texture results with the level of detail present in the input images from
sparse multi-view RGB cameras. In this paper, we propose NeuralHumanFVV, a
real-time neural human performance capture and rendering system to generate
both high-quality geometry and photo-realistic texture of human activities in
arbitrary novel views. We propose a neural geometry generation scheme with a
hierarchical sampling strategy for real-time implicit geometry inference, as
well as a novel neural blending scheme to generate high resolution (e.g., 1k)
and photo-realistic texture results in the novel views. Furthermore, we adopt
neural normal blending to enhance geometry details and formulate our neural
geometry and texture rendering into a multi-task learning framework. Extensive
experiments demonstrate the effectiveness of our approach to achieve
high-quality geometry and photo-realistic free view-point reconstruction for
challenging human performances.
