MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

CGANs are generative models that depend on Deep Learning and can generate images that meet given conditions. However, if a network has a deep architecture, conditions do not provide enough information, so unnatural images are generated. In this paper, we propose a facial image generation method by introducing weighted conditions to CGANs. Weighted condition vectors are input in each layer of a generator, and then a discriminator is extend to multi-tasks so as to recognize input conditions. This approach can step-by-step reflect conditions inputted to the generator at every layer, fulfill the input conditions, and generate high quality images. We demonstrate the effectiveness of our method in both subjective and objective evaluation experiments.