  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.
The project website with video, code, and data can be found at
https://akanazawa.github.io/human_dynamics/.
