Dual-reference Age Synthesis

Age synthesis has received much attention in recent years. State-of-the-art methods typically take an input image and utilize a numeral to control the age of the generated image. In this paper, we revisit the age synthesis and ask: is a numeral capable enough to describe the human age? We propose a new framework Dual-reference Age Synthesis (DRAS) that takes two images as inputs to generate an image which shares the same personality of the first image and has the similar age with the second image. In the proposed framework, we employ a joint manifold feature which consists of disentangled age and identity information. The final images are generated by training a generative adversarial network which competes against an age agent and an identity agent. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.