  We propose POse-guided SElective Fusion (POSEFusion), a single-view human
volumetric capture method that leverages tracking-based methods and
tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction.
By contributing a novel reconstruction framework which contains pose-guided
keyframe selection and robust implicit surface fusion, our method fully
utilizes the advantages of both tracking-based methods and tracking-free
inference methods, and finally enables the high-fidelity reconstruction of
dynamic surface details even in the invisible regions. We formulate the
keyframe selection as a dynamic programming problem to guarantee the temporal
continuity of the reconstructed sequence. Moreover, the novel robust implicit
surface fusion involves an adaptive blending weight to preserve high-fidelity
surface details and an automatic collision handling method to deal with the
potential self-collisions. Overall, our method enables high-fidelity and
dynamic capture in both visible and invisible regions from a single RGBD
camera, and the results and experiments show that our method outperforms
state-of-the-art methods.
