MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation

CVPR 2020 Yuheng LiKrishna Kumar SinghUtkarsh OjhaYong Jae Lee

We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We build upon FineGAN, an unconditional generative model, to learn the desired disentanglement and image generator, and leverage adversarial joint image-code distribution matching to learn the latent factor encoders... (read more)

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