We propose DoubleFusion, a new real-time system that combines volumetric
dynamic reconstruction with data-driven template fitting to simultaneously
reconstruct detailed geometry, non-rigid motion and the inner human body shape
from a single depth camera. One of the key contributions of this method is a
double layer representation consisting of a complete parametric body shape
inside, and a gradually fused outer surface layer. A pre-defined node graph on
the body surface parameterizes the non-rigid deformations near the body, and a
free-form dynamically changing graph parameterizes the outer surface layer far
from the body, which allows more general reconstruction. We further propose a
joint motion tracking method based on the double layer representation to enable
robust and fast motion tracking performance. Moreover, the inner body shape is
optimized online and forced to fit inside the outer surface layer. Overall, our
method enables increasingly denoised, detailed and complete surface
reconstructions, fast motion tracking performance and plausible inner body
shape reconstruction in real-time. In particular, experiments show improved
fast motion tracking and loop closure performance on more challenging
scenarios.

DoubleFusion 提出了一种新的实时系统，将体积动态重建与数据驱动的模板拟合相结合，能够同时从单个深度相机中重建详细的几何形状、非刚性运动和人体内部形状，并实现快速运动跟踪性能。