RSVP

Face aging, which renders aging faces for an input face, has a racted extensive a ention in the multimedia research. Recently, several conditional Generative Adversarial Nets (GANs) based methods have achieved great success. ey can generate images ing the real face distributions conditioned on each individual age group. However, these methods fail to capture the transition pa erns, e.g., the gradual shape and texture changes between adjacent age groups. In this paper, we propose a novel Contextual Generative Adversarial Nets (C-GANs) to speci cally take it into consideration. e C-GANs consists of a conditional transformation network and two discriminative networks. e conditional transformation network imitates the aging procedure with several specially designed residual blocks. e age discriminative network guides the synthesized face to t the real conditional distribution. e transition pa ern discriminative network is novel, aiming to distinguish the real transition pa erns with the fake ones. It serves as an extra regularization term for the conditional transformation network, ensuring the generated image pairs to t the corresponding real transition pa ern distribution. Experimental results demonstrate the proposed framework produces appealing results by comparing with the state-of-the-art and ground truth. We also observe performance gain for cross-age face veri cation.

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Trusted Guidance Pyramid Network for Human Parsing

Luo

Zhang

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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RSVP

Cited by 5 publications

References 4 publications

Pose-Guided Hierarchical Semantic Decomposition and Composition for Human Parsing

Pose-Guided Hierarchical Semantic Decomposition and Composition for Human Parsing

Face Aging with Contextual Generative Adversarial Nets

Trusted Guidance Pyramid Network for Human Parsing

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