Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos

We propose a novel approach that disentangles the identity and pose of objects for image generation. Our model takes as input an ID image and a pose image, and generates an output image with the identity of the ID image and the pose of the pose image. Unlike most previous unsupervised work which rely on cyclic constraints, which can often be brittle, we instead propose to learn this in a self-supervised way. Specifically, we leverage unlabeled videos to automatically construct pseudo ground-truth targets to directly supervise our model. To enforce disentanglement, we propose a novel disentanglement loss, and to improve realism, we propose a pixel-verification loss in which the generated image's pixels must trace back to the ID input. We conduct extensive experiments on both synthetic and real images to demonstrate improved realism, diversity, and ID/pose disentanglement compared to existing methods.