First Order Motion Model for Image Animation

Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this problem without using any annotation or prior information about the specific object to animate... Once trained on a set of videos depicting objects of the same category (e.g. faces, human bodies), our method can be applied to any object of this class. To achieve this, we decouple appearance and motion information using a self-supervised formulation. To support complex motions, we use a representation consisting of a set of learned keypoints along with their local affine transformations. A generator network models occlusions arising during target motions and combines the appearance extracted from the source image and the motion derived from the driving video. Our framework scores best on diverse benchmarks and on a variety of object categories. Our source code is publicly available. read more

PDF Abstract NeurIPS 2019 PDF NeurIPS 2019 Abstract

Datasets


Introduced in the Paper:

Tai-Chi-HD

Used in the Paper:

VoxCeleb1 MGif

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Video Reconstruction Tai-Chi-HD First Order Motion L1 0.063 # 1

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