Human volumetric capture is a long-standing topic in computer vision and computer graphics. Although high-quality results can be achieved using sophisticated off-line systems, real-time human volumetric capture of complex scenarios, especially using light-weight setups, remains challenging. In this paper, we propose a human volumetric capture method that combines temporal volumetric fusion and deep implicit functions. To achieve high-quality and temporal-continuous reconstruction, we propose dynamic sliding fusion to fuse neighboring depth observations together with topology consistency. Moreover, for detailed and complete surface generation, we propose detail-preserving deep implicit functions for RGBD input which can not only preserve the geometric details on the depth inputs but also generate more plausible texturing results. Results and experiments show that our method outperforms existing methods in terms of view sparsity, generalization capacity, reconstruction quality, and run-time efficiency.

翻译：人类体积捕捉是计算机视觉和计算机图形中长期存在的主题。虽然使用复杂的离线系统可以实现高质量的结果,但实时的人类体积捕捉复杂情景,特别是使用轻量量级设置,仍然具有挑战性。在本文件中,我们提议了一种人体体积捕捉方法,将时间体积聚合和深层隐含功能结合起来。为了实现高质量和时间持续性的重建,我们提议了动态滑动聚合,将相邻深度观测与地形一致性结合起来。此外,对于详细和完整的地表生成,我们提议为RGBD输入保留深层的隐含功能,这些功能不仅能够保存深度投入的几何细节,而且能够产生更合理的纹理结果。结果和实验表明,我们的方法在视觉宽度、一般化能力、重建质量和运行时间效率方面超过了现有的方法。