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STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing | | Ming Liu
; Yukang Ding
; Min Xia
; Xiao Liu
; Errui Ding
; Wangmeng Zuo
; Shilei Wen
; | | Date: |
22 Apr 2019 | | Abstract: | Arbitrary attribute editing generally can be tackled by incorporating
encoder-decoder and generative adversarial networks. However, the bottleneck
layer in encoder-decoder usually gives rise to blurry and low quality editing
result. And adding skip connections improves image quality at the cost of
weakened attribute manipulation ability. Moreover, existing methods exploit
target attribute vector to guide the flexible translation to desired target
domain. In this work, we suggest to address these issues from selective
transfer perspective. Considering that specific editing task is certainly only
related to the changed attributes instead of all target attributes, our model
selectively takes the difference between target and source attribute vectors as
input. Furthermore, selective transfer units are incorporated with
encoder-decoder to adaptively select and modify encoder feature for enhanced
attribute editing. Experiments show that our method (i.e., STGAN)
simultaneously improves attribute manipulation accuracy as well as perception
quality, and performs favorably against state-of-the-arts in arbitrary facial
attribute editing and season translation. | | Source: | arXiv, 1904.9709 | | Services: | Forum | Review | PDF | Favorites | |
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