LiveCap
Citations Over TimeTop 1% of 2019 papers
Abstract
We present the first real-time human performance capture approach that reconstructs dense, space-time coherent deforming geometry of entire humans in general everyday clothing from just a single RGB video. We propose a novel two-stage analysis-by-synthesis optimization whose formulation and implementation are designed for high performance. In the first stage, a skinned template model is jointly fitted to background subtracted input video, 2D and 3D skeleton joint positions found using a deep neural network, and a set of sparse facial landmark detections. In the second stage, dense non-rigid 3D deformations of skin and even loose apparel are captured based on a novel real-time capable algorithm for non-rigid tracking using dense photometric and silhouette constraints. Our novel energy formulation leverages automatically identified material regions on the template to model the differing non-rigid deformation behavior of skin and apparel. The two resulting non-linear optimization problems per frame are solved with specially tailored data-parallel Gauss-Newton solvers. To achieve real-time performance of over 25Hz, we design a pipelined parallel architecture using the CPU and two commodity GPUs. Our method is the first real-time monocular approach for full-body performance capture. Our method yields comparable accuracy with off-line performance capture techniques while being orders of magnitude faster.
Related Papers
- → High fidelity facial animation capture and retargeting with contours(2013)51 cited
- → Experimenting with nonintrusive motion capture in a virtual environment(2001)7 cited
- → Input Device—Motion Capture(2017)4 cited
- → Model Retargeting Motion Capture System Based on Kinect Gesture Calibration(2020)
- → Intelligent Techniques for Character Animation(2008)