• Corpus ID: 236950415

Disentangled Lifespan Face Synthesis

@article{He2021DisentangledLF,
  title={Disentangled Lifespan Face Synthesis},
  author={Sen He and Wentong Liao and Michael Ying Yang and Yi-Zhe Song and Bodo Rosenhahn and Tao Xiang},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.02874}
}
A lifespan face synthesis (LFS) model aims to generate a set of photo-realistic face images of a person’s whole life, given only one snapshot as reference. The generated face image given a target age code is expected to be agesensitive reflected by bio-plausible transformations of shape and texture, while being identity preserving. This is extremely challenging because the shape and texture characteristics of a face undergo separate and highly nonlinear transformations w.r.t. age. Most recent… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 47 REFERENCES
Learning Face Age Progression: A Pyramid Architecture of GANs
TLDR
A novel generative adversarial network based approach that separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable.
Face Aging with Identity-Preserved Conditional Generative Adversarial Networks
TLDR
An Identity-Preserved Conditional Generative Adversarial Networks (IPCGANs) framework is proposed, in which an identity-preserved module preserves the identity information and an age classifier forces the generated face with the target age.
Interpreting the Latent Space of GANs for Semantic Face Editing
TLDR
This work proposes a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs, and finds that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations.
Face aging with conditional generative adversarial networks
TLDR
This work proposes the first GAN-based method for automatic face aging and introduces a novel approach for “Identity-Preserving” optimization of GAN's latent vectors.
Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition
TLDR
A novel generative probabilistic model, named Temporal Non-Volume Preserving (TNVP) transformation, is presented to model the facial aging process at each stage, and shows its advantages not only in capturing the non-linear age related variance in each stage but also producing a smooth synthesis in age progression across faces.
Reconstruction-Based Disentanglement for Pose-Invariant Face Recognition
TLDR
This paper presents a method for learning a feature representation that is invariant to pose, without requiring extensive pose coverage in training data, and proposes a new feature reconstruction metric learning to explicitly disentangle identity and pose.
Age Progression/Regression by Conditional Adversarial Autoencoder
  • Zhifei Zhang, Yang Song, H. Qi
  • Computer Science, Mathematics
    2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
TLDR
A conditional adversarial autoencoder that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously is proposed, and the appealing performance and flexibility of the proposed framework is demonstrated by comparing with the state-of-the-art and ground truth.
Instance-level Facial Attributes Transfer with Geometry-Aware Flow
TLDR
This work proposes the use of geometry-aware flow, which serves as a well-suited representation for modeling the transformation between instance-level facial attributes, and leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed.
AttGAN: Facial Attribute Editing by Only Changing What You Want
TLDR
The proposed method is extended for attribute style manipulation in an unsupervised manner and outperforms the state-of-the-art on realistic attribute editing with other facial details well preserved.
CAFE-GAN: Arbitrary Face Attribute Editing with Complementary Attention Feature
TLDR
A novel GAN model is proposed which is designed to edit only the parts of a face pertinent to the target attributes by the concept of Complementary Attention Feature (CAFE), which identifies the facial regions to be transformed by considering both target attributes as well as complementary attributes.
...
1
2
3
4
5
...