Browsing by Author "Mo, Haoran"

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    Controllable Garment Image Synthesis Integrated with Frequency Domain Features
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Liang, Xinru; Mo, Haoran; Gao, Chengying; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Using sketches and textures to synthesize garment images is able to conveniently display the realistic visual effect in the design phase, which greatly increases the efficiency of fashion design. Existing garment image synthesis methods from a sketch and a texture tend to fail in working on complex textures, especially those with periodic patterns. We propose a controllable garment image synthesis framework that takes as inputs an outline sketch and a texture patch and generates garment images with complicated and diverse texture patterns. To improve the performance of global texture expansion, we exploit the frequency domain features in the generative process, which are from a Fast Fourier Transform (FFT) and able to represent the periodic information of the patterns. We also introduce a perceptual loss in the frequency domain to measure the similarity of two texture pattern patches in terms of their intrinsic periodicity and regularity. Comparisons with existing approaches and sufficient ablation studies demonstrate the effectiveness of our method that is capable of synthesizing impressive garment images with diverse texture patterns while guaranteeing proper texture expansion and pattern consistency.
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    Multi-instance Referring Image Segmentation of Scene Sketches based on Global Reference Mechanism
    (The Eurographics Association, 2022) Ling, Peng; Mo, Haoran; Gao, Chengying; Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-Tak
    Scene sketch segmentation based on referring expression plays an important role in sketch editing of anime industry. While most existing referring image segmentation approaches are designed for the standard task of generating a binary segmentation mask for a single or a group of target(s), we think it necessary to equip these models with the ability of multi-instance segmentation. To this end, we propose GRM-Net, a one-stage framework tailored for multi-instance referring image segmentation of scene sketches. We extract the language features from the expression and fuse it into a conventional instance segmentation pipeline for filtering out the undesired instances in a coarse-to-fine manner and keeping the matched ones. To model the relative arrangement of the objects and the relationship among them from a global view, we propose a global reference mechanism (GRM) to assign references to each detected candidate to identify its position. We compare with existing methods designed for multi-instance referring image segmentation of scene sketches and for the standard task of referring image segmentation, and the results demonstrate the effectiveness and superiority of our approach.