Learning 3D Human Dynamics from Video


Angjoo Kanazawa* Jason Y. Zhang* Panna Felsen*
Jitendra Malik
University of California, Berkeley

[Paper]
[Video]



From a video of a human, our model (blue) can predict 3D meshes that are more temporally consistent than a method that only uses a single view (pink).
From a single image (purple), our model can recovers the current 3D mesh as well as the past and future 3D poses.


From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model, Human Mesh and Motion Recovery (HMMR), on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning.

Paper

Kanazawa*, Zhang*, Felsen*, and Malik.

Learning 3D Human Dynamics from Video.

CVPR 2019.
[pdf]     [Bibtex]

Project Video

Code


 [GitHub]

Demo Results


More Results

Acknowledgements

We thank David Fouhey for providing us with the people subset of VLOG, Rishabh Dabral for providing the source code for TP-Net, Timo von Marcard and Gerard Pons-Moll for help with 3DPW, and Heather Lockwood for her help and support. This work was supported in part by Intel/NSF VEC award IIS-1539099 and BAIR sponsors. This webpage template was borrowed from some colorful folks.