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  • G F : FINE GRAINEDTEXT AWAREFASH IONI EDITING VIADIFFUSIONMODELS
    We introduce a unified Text-Aware Conditioning Module (TACM) that explicitly models glyph strokes and spatial layouts, incorporating an OCR-aware perceptual loss to enhance character readability and semantic correctness, thereby achieving fine-grained and control-lable text generation on garments
  • CVPR2024 | 图片生成与编辑相关的AIGC文章汇总 - 知乎
    在这个博客中，为大家精心整理了CVPR2024中关于图像生成和编辑领域的研究工作，并附上了各篇论文的链接以及部分工作的代码链接。 如果您对图片生成和编辑领域感兴趣或有相关需求，这里将是一个便捷的导航资源，助您…
  • TexFit: Text-Driven Fashion Image Editing with Diffusion Models
    In this paper, we propose TexFit, a Tex t-driven F ashion i mage Edi t ing method using diffusion models, which performs the local image editing only with the easily accessible text
  • Search | OpenReview
    FantasyPortrait: Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers Wang Qiang, Mengchao Wang, Fan Jiang, Yaqi Fan, Yonggang Qi, Mu Xu
  • DPDEdit: Detail-Preserved Diffusion Models for Multimodal Fashion Image . . .
    DPDEdit guides the fashion image generation of diffusion models by integrating text prompts, region masks, human pose images, and garment texture images
  • Texture-preserving multimodal fashion image editing with diffusion models
    This approach enables localized fine-grained editing of fashion images by incorporating modal information such as text descriptions, human denseposes, garment sketches, and fabric textures
  • Jinxia Zhang - catalyzex. com
    Existing models often fail to leverage these fine-grained attributes from both text and image modalities To address the above issues, we propose a novel approach for the fashion domain, Fine-grained Attributes Enhanced VLP (FashionFAE), which focuses on the detailed characteristics of fashion data
  • CVPR Poster Text-Driven Fashion Image Editing with Compositional . . .
    Fashion image editing is a valuable tool for designers to convey their creative ideas by visualizing design concepts With the recent advances in text editing methods, significant progress has been made in fashion image editing
  • TexFit: Text-Driven Fashion Image Editing with Diffusion Models
    在本文中，我们提出了TexFit，这是一种使用扩散模型的文本驱动的时尚图像编辑方法，该方法仅使用常见的文本描述便能进行局部图像编辑。 我们的方法使用一个基于文本的编辑区域模块来预测时尚图像中的精确编辑区域。 然后，我们将预测区域作为扩散模型的生成条件，结合文本提示，在保持其余部分不变的同时，实现对时尚图像的精确局部编辑。 此外，以往的时尚数据集侧重于全局描述，缺乏能够指导精确局部编辑的局部描述性信息。 因此，我们利用区域提取和属性组合策略来开发一种新的DFMM-Spotlight数据集，它局部地专注于服装和配饰，使文本输入进行本地编辑实现。 在DFMM-Spotlight数据集上的实验结果验证了该模型的有效性。
  • Taming Diffusion for Fashion Clothing Generation with Versatile . . .
    We introduce S2CDiff, a novel framework leveraging lightweight adapters to enable fine-grained control over pre-trained T2I diffusion models It extends the capabilities of ControlNet by enabling clothing image generation, incorporating various style guidance such as sketch and color





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