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Topology-Invariant Synthesis

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Heterogeneous Facial Analysis and Synthesis

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

To narrow the inherent gap between heterogeneous face domains, much efforts have been made in the past decade. Some of these solutions resort to deep generative networks to synthesize proper face images based on inputs from another domain. It is widely agreed that facial topology tends to be closely related to identity information, since facial appearance has been deemed as a crucial feature for identifying individuals. A small topological change on the face may make one person become another. Taking makeup removal as an example, the synthesized output is expected to resemble the input except for the makeup effects, and the facial topology is premier to be preserved during synthesis. We generalize this kind of problem as topology-invariant facial synthesis. In this chapter, we select several representative topology-invariant synthesis tasks and elaborate their latest progress. These tasks include cross-spectral face hallucination, makeup removal and age progression/regression. For each task, we start from its background and characteristics, followed by the illustration of latest methods as well as their experimental outcomes.

Part of this chapter is reprinted from Song et al. [30], Li et al. [18, 20,21,22] with permission from AAAI, Springer and IEEE.

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Notes

  1. 1.

    https://github.com/ly-joy/Cross-Makeup-Face

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yi Li, Huaibo Huang, Ran He & Tieniu Tan

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  1. Yi Li
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  2. Huaibo Huang
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  3. Ran He
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  4. Tieniu Tan
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Correspondence to Yi Li .

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Li, Y., Huang, H., He, R., Tan, T. (2020). Topology-Invariant Synthesis. In: Heterogeneous Facial Analysis and Synthesis. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-9148-4_3

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  • DOI: https://doi.org/10.1007/978-981-13-9148-4_3

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  • Online ISBN: 978-981-13-9148-4

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