Name
Abstract | ||
|---|---|---|
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(1). |
Year | Venue | Keywords |
|---|---|---|
2019 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) | prior information,human bodies |
Field | DocType | Volume |
Computer graphics (images),Computer science,First order,Animation,Artificial intelligence,Machine learning | Conference | 32 |
ISSN | Citations | PageRank |
1049-5258 | 2 | 0.40 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aliaksandr Siarohin | 1 | 28 | 3.06 |
| Stephane Lathuillere | 2 | 2 | 0.40 |
| Sergey Tulyakov | 3 | 28 | 9.28 |
| Elisa Ricci 0002 | 4 | 1393 | 73.75 |
| Nicu Sebe | 5 | 7013 | 403.03 |
| Lathuilière, Stéphane | 6 | 2 | 0.40 |