NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering using RGB Cameras

• URL: http://arxiv.org/abs/2103.07700v1
• Date: Sat, 13 Mar 2021 12:03:38 GMT
• Title: NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering using RGB Cameras
• Authors: Xin Suo and Yuheng Jiang and Pei Lin and Yingliang Zhang and Kaiwen Guo and Minye Wu and Lan Xu
• Abstract summary: 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. We propose 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.
• Score: 17.18904717379273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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