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DoubleFusion: Real-Time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor

We propose DoubleFusion, a new real-time system that combines volumetric non-rigid reconstruction with data-driven template fitting to simultaneously reconstruct detailed surface geometry, large non-rigid motion and the optimized 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 model inside, and a gradually fused detailed surface outside. A pre-defined node graph on the body 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 parametric body is optimized online and forced to fit inside the outer surface layer as well as the live depth input. Overall, our method enables increasingly denoised, detailed and complete surface reconstructions, fast motion tracking performance and plausible inner body shape reconstruction in real-time. Experiments and comparisons show improved fast motion tracking and loop closure performance on more challenging scenarios. Two extended applications including body measurement and shape retargeting show the potential of our system in terms of practical use.