  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. MixNMatch requires
bounding boxes during training to model background, but requires no other
supervision. Through extensive experiments, we demonstrate MixNMatch's ability
to accurately disentangle, encode, and combine multiple factors for
mix-and-match image generation, including sketch2color, cartoon2img, and
img2gif applications. Our code/models/demo can be found at
https://github.com/Yuheng-Li/MixNMatch
