Date: July 2017.
Source: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, pp 5573-5582, doi: 10.1109/CVPR.2017.591.
Abstract: While the ready availability of 3D scan data has influenced research throughout computer vision, less attention has focused on 4D data, that is 3D scans of moving non-rigid objects, captured over time. To be useful for vision research, such 4D scans need to be registered, or aligned, to a common topology. Consequently, extending mesh registration methods to 4D is important. Unfortunately, no ground-truth datasets are available for quantitative evaluation and comparison of 4D registration methods. To address this we create a novel dataset of high-resolution 4D scans of human subjects in motion, captured at 60 fps. We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology. The approach exploits consistency in texture over both short and long time intervals and deals with temporal offsets between shape and texture capture. We show how using geometry alone results in significant errors in alignment when the motions are fast and non-rigid. We evaluate the accuracy of our registration and provide a dataset of 40,000 raw and aligned meshes. Dynamic FAUST extends the popular FAUST dataset to dynamic 4D data, and is available for research purposes at http://dfaust.is.tue.mpg.de.
3dMD’s Role: Dynamic FAUST’s real-world 4D data of 129 sequences and over 40,000 frames across 10 subjects, was captured at 60 FPS using a dynamic-3D/4D active stereophotogrammetry 3dMDbody22.u System with 22 Modular Camera Units (MCUs) each with a stereo camera pair, color camera, and a speckle projector, supplemented by 12 additional standalone speckle projectors for a total of 34. This research is a dynamic-3D/4D real-world data extension of the original static-3D FAUST dataset published in 2014.

Article: Dynamic FAUST: Registering Human Bodies in Motion.
Authors: Federica Bogo, Javier Romero, Gerard Pons-Moll, Michael J Black. MPI for Intelligent Systems, Perceiving Systems, Tübingen, Germany.

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