Forecasting Human Dynamics from Static Images

Yu-Wei Chao1       Jimei Yang2       Brian Price2       Scott Cohen2       Jia Deng1

1University of Michigan, Ann Arbor       2Adobe Research

CVPR 2017

Abstract

This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a sequence of upcoming human body poses in 3D. To address the problem, we propose the 3D Pose Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on single-image human pose estimation and sequence prediction, and converts the 2D predictions into 3D space. We train our 3D-PFNet using a three-step training strategy to leverage a diverse source of training data, including image and video based human pose datasets and 3D motion capture (MoCap) data. We demonstrate competitive performance of our 3D-PFNet on 2D pose forecasting and 3D structure recovery through quantitative and qualitative results.

Forecasting 2D Poses

Below are some selected animations showing the forecasted 2D poses generated by our model. Note that the action labels are not used in obtaining the results, but are shown here just for the visualization purpose.

Bowl

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Clean and Jerk

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Bench Press

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Golf Swing

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Baseball Swing

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Baseball Pitch

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Pullup

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Pushup

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Situp

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Jump Rope

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Jumping Jacks

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Squat

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Strum Guitar

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Tennis Forehand

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Tennis Serve

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Recovering 3D Pose and Rendering Human Character

Our model also converts the forecasted 2D skeletal poses into 3D space. For better interpreteration, we render human characters from the output 3D skeletal poses using the public code provided by Chen et al. [1].

Forecasted 2D Pose Forecasted 3D Pose Rendered Human Character Ground-truth
Frame & Pose
Forecasted 2D Pose Forecasted 3D Pose Rendered Human Character Ground-truth
Frame & Pose
Forecasted 2D Pose Forecasted 3D Pose Rendered Human Character Ground-truth
Frame & Pose
Forecasted 2D Pose Forecasted 3D Pose Rendered Human Character Ground-truth
Frame & Pose
Forecasted 2D Pose Forecasted 3D Pose Rendered Human Character Ground-truth
Frame & Pose
Forecasted 2D Pose Forecasted 3D Pose Rendered Human Character Ground-truth
Frame & Pose
Forecasted 2D Pose Forecasted 3D Pose Rendered Human Character Ground-truth
Frame & Pose
Forecasted 2D Pose Forecasted 3D Pose Rendered Human Character Ground-truth
Frame & Pose

Paper

Forecasting Human Dynamics from Static Images.
Yu-Wei Chao, Jimei Yang, Brian Price, Scott Cohen, and Jia Deng.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[paper] [supplementary material] [arXiv] [bibtex]


References

  1. W. Chen, H. Wang, Y. Li, H. Su, Z. Wang, C. Tu, D. Lischinski, D. Cohen-Or, and B. Chen. Synthesizing training images for boosting human 3d pose estimation. In 3DV, 2016.

Contact

Send any comments or questions to Yu-Wei Chao: ywchao@umich.edu.


Last updated on 2017/04/11