ControlEdit: A MultiModal Local Clothing Image Editing Method

• URL: http://arxiv.org/abs/2409.14720v1
• Date: Mon, 23 Sep 2024 05:34:59 GMT
• Title: ControlEdit: A MultiModal Local Clothing Image Editing Method
• Authors: Di Cheng, YingJie Shi, ShiXin Sun, JiaFu Zhang, WeiJing Wang, Yu Liu,
• Abstract summary: Multimodal clothing image editing refers to the precise adjustment and modification of clothing images using data such as textual descriptions and visual images as control conditions. We propose a new image editing method ControlEdit, which transfers clothing image editing to multimodal-guided local inpainting of clothing images.
• Score: 3.6604114810930946
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal clothing image editing refers to the precise adjustment and modification of clothing images using data such as textual descriptions and visual images as control conditions, which effectively improves the work efficiency of designers and reduces the threshold for user design. In this paper, we propose a new image editing method ControlEdit, which transfers clothing image editing to multimodal-guided local inpainting of clothing images. We address the difficulty of collecting real image datasets by leveraging the self-supervised learning approach. Based on this learning approach, we extend the channels of the feature extraction network to ensure consistent clothing image style before and after editing, and we design an inverse latent loss function to achieve soft control over the content of non-edited areas. In addition, we adopt Blended Latent Diffusion as the sampling method to make the editing boundaries transition naturally and enforce consistency of non-edited area content. Extensive experiments demonstrate that ControlEdit surpasses baseline algorithms in both qualitative and quantitative evaluations.

Related papers

• PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models [80.98455219375862]
  We present the first text-based image editing approach for object parts based on pre-trained diffusion models. Our approach is preferred by users 77-90% of the time in conducted user studies.
  arXiv  Detail & Related papers  (2025-02-06T13:08:43Z)
• Edicho: Consistent Image Editing in the Wild [90.42395533938915]
  Edicho steps in with a training-free solution based on diffusion models. It features a fundamental design principle of using explicit image correspondence to direct editing.
  arXiv  Detail & Related papers  (2024-12-30T16:56:44Z)
• UIP2P: Unsupervised Instruction-based Image Editing via Cycle Edit Consistency [69.33072075580483]
  We propose an unsupervised model for instruction-based image editing that eliminates the need for ground-truth edited images during training. Our method addresses these challenges by introducing a novel editing mechanism called Cycle Edit Consistency ( CEC) CEC applies forward and backward edits in one training step and enforces consistency in image and attention spaces.
  arXiv  Detail & Related papers  (2024-12-19T18:59:58Z)
• Streamlining Image Editing with Layered Diffusion Brushes [8.738398948669609]
  Our system renders a single edit on a 512x512 image within 140 ms using a high-end consumer GPU. Our approach demonstrates efficacy across a range of tasks, including object attribute adjustments, error correction, and sequential prompt-based object placement and manipulation.
  arXiv  Detail & Related papers  (2024-05-01T04:30:03Z)
• Optimisation-Based Multi-Modal Semantic Image Editing [58.496064583110694]
  We propose an inference-time editing optimisation to accommodate multiple editing instruction types. By allowing to adjust the influence of each loss function, we build a flexible editing solution that can be adjusted to user preferences. We evaluate our method using text, pose and scribble edit conditions, and highlight our ability to achieve complex edits.
  arXiv  Detail & Related papers  (2023-11-28T15:31:11Z)
• LayerDiffusion: Layered Controlled Image Editing with Diffusion Models [5.58892860792971]
  LayerDiffusion is a semantic-based layered controlled image editing method. We leverage a large-scale text-to-image model and employ a layered controlled optimization strategy. Experimental results demonstrate the effectiveness of our method in generating highly coherent images.
  arXiv  Detail & Related papers  (2023-05-30T01:26:41Z)
• StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing [115.49488548588305]
  A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images. They either finetune the model, or invert the image in the latent space of the pretrained model. They suffer from two problems: Unsatisfying results for selected regions and unexpected changes in non-selected regions.
  arXiv  Detail & Related papers  (2023-03-28T00:16:45Z)
• Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation [136.53288628437355]
  Controllable semantic image editing enables a user to change entire image attributes with few clicks. Current approaches often suffer from attribute edits that are entangled, global image identity changes, and diminished photo-realism. We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work which primarily focuses on qualitative evaluation.
  arXiv  Detail & Related papers  (2021-02-01T21:38:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.