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Conference Papers Year : 2019

Human Hair Segmentation In The Wild Using Deep Shape Prior

Yongzhe Yan
Xavier Naturel
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Thierry Chateau

Abstract

Virtual human hair dying is becoming a popular Augmented Reality (AR) application in recent years. Human hair contains diverse color and texture information which can be significantly varied from case to case depending on different hair styles and environmental lighting conditions. However, the publicly available hair segmentation datasets are relatively small. As a result, hair segmentation can be easily interfered by the cluttered background in practical use. In this paper, we propose to integrate a shape prior into Fully Convolutional Neural Network (FCNN) to mitigate this issue. First, we utilize a FCNN with an Atrous Spatial Pyramid Pooling (ASPP) module [1] to find a human hair shape prior based on a specific distance transform. In the second stage, we combine the hair shape prior and the original image to form the input of a symmetric encoder-decoder FCNN to get the final hair segmentation output. Both quantitative and qualitative results show that our method achieves state-of-the-art performance on the publicly available LFW-Part and Figaro1k datasets.
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Dates and versions

hal-02891974 , version 1 (07-07-2020)

Identifiers

  • HAL Id : hal-02891974 , version 1

Cite

Yongzhe Yan, Anthony Berthelier, Stefan Duffner, Xavier Naturel, Christophe Garcia, et al.. Human Hair Segmentation In The Wild Using Deep Shape Prior. Conference on Computer Vision and Pattern Recognition Workshop (CVPR Workshop), Jun 2019, Long Beach, United States. ⟨hal-02891974⟩
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