Deep Video‐Based Performance Cloning

We present a new video‐based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving...

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Bibliographic Details
Published in:Computer graphics forum Vol. 38; no. 2; pp. 219 - 233
Main Authors: Aberman, K., Shi, M., Liao, J., Liscbinski, D., Chen, B., Cohen‐Or, D.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.05.2019
Subjects:
Artificial neural networks
CCS Concepts
Cloning
Computing methodologies → Image‐based rendering
Dance
Motion capture
Neural networks
Target recognition
Training
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:We present a new video‐based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving performances are provided as ordinary video segments, without motion capture or depth information. Our generative model is realized as a deep neural network with two branches, both of which train the same space‐time conditional generator, using shared weights. One branch, responsible for learning to generate the appearance of the target actor in various poses, uses paired training data, self‐generated from the reference video. The second branch uses unpaired data to improve generation of temporally coherent video renditions of unseen pose sequences. Through data augmentation, our network is able to synthesize images of the target actor in poses never captured by the reference video. We demonstrate a variety of promising results, where our method is able to generate temporally coherent videos, for challenging scenarios where the reference and driving videos consist of very different dance performances.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13632