PyTorch implementation of Intrinsic-Extrinsic Preserved Generative Adversarial Network (IEP-GAN) for both intrinsic (i.e., shape) and extrinsic (i.e., pose) information preservation. Extrinsically, a co-occurrence discriminator is used to capture the stru... read more of IEP-GAN: Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer

Python implementation of novel Cycle In Cycle Generative Adversarial Network (C2GAN) for the task of keypoint-guided image generation. The C2GAN is a cross-modal framework exploring a joint exploitation of the keypoint and the image data in an interactive... read more of Cycle-In-Cycle GANs

Novel two-stage framework with a new Cascaded Cross MLP-Mixer (CrossMLP) sub-network in the first stage and one refined pixel-level loss in the second stage. In the first stage, the CrossMLP sub-network learns the latent transformation cues between image ... read more of Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation

Training time procedures to produce secret adversarial backdoors in Deep Generative Models read more of The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks