Publication
GANimation: One-shot anatomically consistent facial animation
Journal Article (2020)
Journal
International Journal of Computer Vision
Pages
698-713
Volume
128
Doc link
https://doi.org/10.1007/s11263-019-01210-3
File
Authors
Projects associated
AEROARMS: AErial RObotics System integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance
ColRobTransp: Colaboración robots-humanos para el transporte de productos en zonas urbanas
MdM: Unit of Excellence María de Maeztu
HuMoUR: Markerless 3D human motion understanding for adaptive robot behavior
Amazon ResearchAward: Geometry-aware 3D Human Body Animation from Still Photos
Abstract
Recent advances in generative adversarial networks (GANs) have shown impressive results for the task of facial expression synthesis. The most successful architecture is StarGAN (Choi et al. in CVPR, 2018), that conditions GANs’ generation process with images of a specific domain, namely a set of images of people sharing the same expression. While effective, this approach can only generate a discrete number of expressions, determined by the content and granularity of the dataset. To address this limitation, in this paper, we introduce a novel GAN conditioning scheme based on action units (AU) annotations, which describes in a continuous manifold the anatomical facial movements defining a human expression. Our approach allows controlling the magnitude of activation of each AU and combining several of them. Additionally, we propose a weakly supervised strategy to train the model, that only requires images annotated with their activated AUs, and exploit a novel self-learned attention mechanism that makes our network robust to changing backgrounds, lighting conditions and occlusions. Extensive evaluation shows that our approach goes beyond competing conditional generators both in the capability to synthesize a much wider range of expressions ruled by anatomically feasible muscle movements, as in the capacity of dealing with images in the wild. The code of this work is publicly available at https://github.com/albertpumarola/GANimation.
Categories
computer vision.
Author keywords
GAN, Face Animation, Action-Unit, Condition
Scientific reference
A. Pumarola, A. Agudo, A.M. Martinez, A. Sanfeliu and F. Moreno-Noguer. GANimation: One-shot anatomically consistent facial animation. International Journal of Computer Vision, 128: 698-713, 2020.
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