HiGarment generates realistic garment images from a flat sketch and text prompt by retrieving fabric references and dynamically weighting sketch versus text cues, outperforming five existing controllable generation methods on a new dataset.
TexControl: Sketch-Based Two-Stage Fashion Image Generation Using Diffusion Model
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abstract
Deep learning-based sketch-to-clothing image generation provides the initial designs and inspiration in the fashion design processes. However, clothing generation from freehand drawing is challenging due to the sparse and ambiguous information from the drawn sketches. The current generation models may have difficulty generating detailed texture information. In this work, we propose TexControl, a sketch-based fashion generation framework that uses a two-stage pipeline to generate the fashion image corresponding to the sketch input. First, we adopt ControlNet to generate the fashion image from sketch and keep the image outline stable. Then, we use an image-to-image method to optimize the detailed textures of the generated images and obtain the final results. The evaluation results show that TexControl can generate fashion images with high-quality texture as fine-grained image generation.
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HiGarment: Cross-modal Harmony Based Diffusion Model for Flat Sketch to Realistic Garment Image
HiGarment generates realistic garment images from a flat sketch and text prompt by retrieving fabric references and dynamically weighting sketch versus text cues, outperforming five existing controllable generation methods on a new dataset.