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Understanding three-dimensional (3D) geometries from two-dimensional (2D)images without any labeled information is promising for understanding the realworld without incurring annotation cost. We herein propose a novel generativemodel, RGBD-GAN, which achieves unsupervised 3D representation learning from 2Dimages. The proposed method enables camera parameter-conditional imagegeneration and depth image generation without any 3D annotations, such ascamera poses or depth. We use an explicit 3D consistency loss for two RGBDimages generated from different camera parameters, in addition to the ordinalGAN objective. The loss is simple yet effective for any type of image generatorsuch as DCGAN and StyleGAN to be conditioned on camera parameters. Throughexperiments, we demonstrated that the proposed method could learn 3Drepresentations from 2D images with various generator architectures.

RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis