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- W4361764044 abstract "The objective of person image synthesis is to generate an image of a person that is perceptually indistinguishable from an actual one. However, the technical challenges that occur in pose transfer, background swapping, and so forth ordinarily lead to an uncontrollable and unpredictable result. This paper proposes a zero-shot synthesis method based on group-supervised learning. The underlying model is a twofold auto-encoder, which first decomposes the latent feature of a target image into a disentangled representation of swappable components and then extracts and recombines the factors therein to synthesize a new person image. Finally, we demonstrate the superiority of our work through both qualitative and quantitative experiments." @default.
- W4361764044 created "2023-04-04" @default.
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- W4361764044 date "2023-01-01" @default.
- W4361764044 modified "2023-10-01" @default.
- W4361764044 title "C-GZS: Controllable Person Image Synthesis Based on Group-Supervised Zero-Shot Learning" @default.
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- W4361764044 doi "https://doi.org/10.1007/978-3-031-27077-2_17" @default.
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