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We introduce LOGAN, a deep neural network aimed at learning general-purposeshape transforms from unpaired domains. The network is trained on two sets ofshapes, e.g., tables and chairs, while there is neither a pairing betweenshapes from the domains as supervision nor any point-wise correspondencebetween any shapes. Once trained, LOGAN takes a shape from one domain andtransforms it into the other. Our network consists of an autoencoder to encodeshapes from the two input domains into a common latent space, where the latentcodes concatenate multi-scale shape features, resulting in an overcompleterepresentation. The translator is based on a generative adversarial network(GAN), operating in the latent space, where an adversarial loss enforcescross-domain translation while a feature preservation loss ensures that theright shape features are preserved for a natural shape transform. We conductablation studies to validate each of our key network designs and demonstratesuperior capabilities in unpaired shape transforms on a variety of examplesover baselines and state-of-the-art approaches. We show that LOGAN is able tolearn what shape features to preserve during shape translation, either local ornon-local, whether content or style, depending solely on the input domains fortraining.

LOGAN: Unpaired Shape Transform in Latent Overcomplete Space