Forecasting Human Dynamics from Static Images Forecasting Human Dynamics from Static Images
Paper summary Problem ------------- Predict human motion from static image http://www-personal.umich.edu/~ywchao/pictures/cvpr2017.png Approach ---------- 1. 2d pose sequence generator 2. convert 2d pose to 3d skeleton https://image.ibb.co/eeBRxv/3D_PFNet.png https://image.ibb.co/kERaVQ/Forecasting_Human_Dynamics_from_Static_Images_architecture.png 3 Step training strategy ------------------------- 1. Train human 2d pose extractor using annotated video with 2d joint positions 2. 3d skeleton extractor: project mocap data to 2d and use as ground truth for training the 2d->3d skeleton converter 3. Full network training Datasets ----------- 1. Penn Action - Annotated human pose in sports image sequences: bench_press, jumping_jacks, pull_ups... 2. MPII - human action videos with annotated single frame 3. Human3.6M - video, depth and mocap. action include: sitting, purchasing, waiting Evaluation ------------- On the following tasks: 1. 2D pose forecasting 2. 3D pose recovery
arxiv.org
scholar.google.com
Forecasting Human Dynamics from Static Images
Chao, Yu-Wei and Yang, Jimei and Price, Brian L. and Cohen, Scott and Deng, Jia
arXiv e-Print archive - 2017 via Bibsonomy
Keywords: dblp


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