• Laser & Optoelectronics Progress
  • Vol. 57, Issue 14, 141011 (2020)
Youwen Huang, Peng Zhao*, and Yadong You
Author Affiliations
  • School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
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    DOI: 10.3788/LOP57.141011 Cite this Article Set citation alerts
    Youwen Huang, Peng Zhao, Yadong You. Pose-Guided Human Image Synthesis Based on Fusion Feature Feedback Mechanism[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141011 Copy Citation Text show less
    Overall architecture of model
    Fig. 1. Overall architecture of model
    DCGAN generator structure including VAE
    Fig. 2. DCGAN generator structure including VAE
    VGG19 network structure for extracting character image information. (a) Process of extracting characteristic information; (b) extract results of characterization information
    Fig. 3. VGG19 network structure for extracting character image information. (a) Process of extracting characteristic information; (b) extract results of characterization information
    Image effect generated by DeepFashion[20] and Market-1501[21] dataset. (a) Generate a person image from a given pose on DeepFashion dataset; (b) generate a person image from a given pose on Market-1501 dataset; (c) generate a series of person images from a given series of poses on DeepFashion
    Fig. 4. Image effect generated by DeepFashion[20] and Market-1501[21] dataset. (a) Generate a person image from a given pose on DeepFashion dataset; (b) generate a person image from a given pose on Market-1501 dataset; (c) generate a series of person images from a given series of poses on DeepFashion
    Test results on DeepFashion and Market-1501 datasets. (a) Condition images; (b) target pose; (c) target images; (d) G1-L1-VGG19; (e) G1-L1; (f) G1-G2-D; (g) G1-G2-VGG19-D
    Fig. 5. Test results on DeepFashion and Market-1501 datasets. (a) Condition images; (b) target pose; (c) target images; (d) G1-L1-VGG19; (e) G1-L1; (f) G1-G2-D; (g) G1-G2-VGG19-D
    Validation results on DeepFashion dataset training model. (a) Condition images; (b) target images; (c) synthesize images
    Fig. 6. Validation results on DeepFashion dataset training model. (a) Condition images; (b) target images; (c) synthesize images
    Validation results on Market-1501 dataset training model. (a) Condition images; (b) target images; (c) synthesize images
    Fig. 7. Validation results on Market-1501 dataset training model. (a) Condition images; (b) target images; (c) synthesize images
    ModelDeepFashionMarket-1501
    SSIMISSSIMISMASK-SSIMMASK-IS
    G1-L10.7352.4270.3043.0060.8092.455
    G1-L1-VGG190.6382.1980.2562.2580.8003.249
    G1-G2-D0.7623.0900.2533.4600.7923.435
    G1-G2-VGG19-D0.7952.7990.2622.9110.7963.520
    Table 1. Comparison of SSIM and IS of different models on different datasets
    ModelDeepFashionMarket-1501
    SSIMISSSIMISMASK-SSIMMASK-IS
    Ref. [10]0.7623.0900.2533.4600.7923.435
    Ref. [26]0.6143.2280.0993.4830.6143.491
    Ref. [27]0.7863.0870.3533.2140.7873.249
    Ours0.7952.7990.2622.9110.7963.520
    Table 2. Comparison of SSIM and IS with other image generation models on different datasets
    DatasetDeepFashionMarket-1501
    Total parameters8.2×1073.6×107
    Train time per epoch/min28.923.44
    Real-data/(sheets·s-1)430
    Table 3. Computational complexity of model
    Youwen Huang, Peng Zhao, Yadong You. Pose-Guided Human Image Synthesis Based on Fusion Feature Feedback Mechanism[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141011
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