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.
This work has been supported by the National Key R &D Program of China under Grant 2019YFE0190500, the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Grant No.2232021D-22), and the Initial Research Funds for Young Teachers of Donghua University.
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Li, J., Gao, Y., Qian, C., Lu, J., Chen, Z. (2023). C-GZS: Controllable Person Image Synthesis Based on Group-Supervised Zero-Shot Learning. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_17
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