Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction

10/04/2019 ∙ by Yunji Kim, et al. ∙ 19

We propose a deep video prediction model conditioned on a single image and an action class. To generate future frames, we first detect keypoints of a moving object and predict future motion as a sequence of keypoints. The input image is then translated following the predicted keypoints sequence to compose future frames. Detecting the keypoints is central to our algorithm, and our method is trained to detect the keypoints of arbitrary objects in an unsupervised manner. Moreover, the detected keypoints of the original videos are used as pseudo-labels to learn the motion of objects. Experimental results show that our method is successfully applied to various datasets without the cost of labeling keypoints in videos. The detected keypoints are similar to human-annotated labels, and prediction results are more realistic compared to the previous methods.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 8

page 9

Code Repositories

Unsupervised-Keypoint-Learning-for-Guiding-Class-conditional-Video-Prediction

An official implementation of the paper "Unsupervised Keypoint Learning for Guiding Class-conditional Video Prediction", NeurIPS 2019


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.