  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.
