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.

本文提出了一种结合时间体积融合和深度隐式函数的人体体积捕捉方法，该方法不仅可以实现高质量、连续的重建，而且可以生成包含几何细节和更加逼真的纹理结果的细节保留深度隐式函数，实验结果表明，该方法在稀疏视角、泛化能力、重建质量和运行效率方面优于现有方法。

Function4D：基于极稀疏消费级 RGBD 传感器的实时人体体积捕获

Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer  RGBD Sensors

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.

提出了一种单视角人体体积捕捉方法，名为 POSEFusion，该方法利用基于跟踪的方法和跟踪自由推断来实现高保真和动态的三维重建。通过新颖的重建框架包含姿势导向的关键帧选择和强大的隐式表面融合，该方法充分利用了基于跟踪的方法和跟踪自由推断方法的优点，并最终实现了动态表面细节的高保真重建，即使在不可见区域内。