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Topology-Invariant Synthesis

  • Yi LiEmail author
  • Huaibo Huang
  • Ran He
  • Tieniu Tan
Chapter
  • 26 Downloads
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

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.

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Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Institute of Automation, Chinese Academy of SciencesBeijingChina

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