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In this paper, we present an Attention-based Identity Preserving Generative Adversarial Network (AIP-GAN) to overcome the identity leakage problem from a source image to a generated face image, an issue that is encountered in a cross-subject facial expression transfer and synthesis process. Our key insight is that the identity preserving network should be able to disentangle and compose shape, appearance, and expression information for efficient facial expression transfer and synthesis.arXiv:2005.00499v1 fatcat:xw2wapx76vbcbguedh34helqyu