Text-Driven Image Editing via Learnable Regions

• URL: http://arxiv.org/abs/2311.16432v2
• Date: Wed, 3 Apr 2024 15:05:28 GMT
• Title: Text-Driven Image Editing via Learnable Regions
• Authors: Yuanze Lin, Yi-Wen Chen, Yi-Hsuan Tsai, Lu Jiang, Ming-Hsuan Yang,
• Abstract summary: We introduce a method for region-based image editing driven by textual prompts, without the need for user-provided masks or sketches. We show that this simple approach enables flexible editing that is compatible with current image generation models. Experiments demonstrate the competitive performance of our method in manipulating images with high fidelity and realism that correspond to the provided language descriptions.
• Score: 74.45313434129005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language has emerged as a natural interface for image editing. In this paper, we introduce a method for region-based image editing driven by textual prompts, without the need for user-provided masks or sketches. Specifically, our approach leverages an existing pre-trained text-to-image model and introduces a bounding box generator to identify the editing regions that are aligned with the textual prompts. We show that this simple approach enables flexible editing that is compatible with current image generation models, and is able to handle complex prompts featuring multiple objects, complex sentences, or lengthy paragraphs. We conduct an extensive user study to compare our method against state-of-the-art methods. The experiments demonstrate the competitive performance of our method in manipulating images with high fidelity and realism that correspond to the provided language descriptions. Our project webpage can be found at: https://yuanze-lin.me/LearnableRegions_page.

Related papers

• Zero-shot Image Editing with Reference Imitation [50.75310094611476]
  We present a new form of editing, termed imitative editing, to help users exercise their creativity more conveniently. We propose a generative training framework, dubbed MimicBrush, which randomly selects two frames from a video clip, masks some regions of one frame, and learns to recover the masked regions using the information from the other frame. We experimentally show the effectiveness of our method under various test cases as well as its superiority over existing alternatives.
  arXiv  Detail & Related papers  (2024-06-11T17:59:51Z)
• Zero-shot Image-to-Image Translation [57.46189236379433]
  We propose pix2pix-zero, an image-to-image translation method that can preserve the original image without manual prompting. We propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process. Our method does not need additional training for these edits and can directly use the existing text-to-image diffusion model.
  arXiv  Detail & Related papers  (2023-02-06T18:59:51Z)
• FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion [16.583537785874604]
  We propose a novel text-conditioned editing model, called FICE, capable of handling a wide variety of diverse text descriptions. FICE generates highly realistic fashion images and leads to stronger editing performance than existing competing approaches.
  arXiv  Detail & Related papers  (2023-01-05T15:33:23Z)
• SpaText: Spatio-Textual Representation for Controllable Image Generation [61.89548017729586]
  SpaText is a new method for text-to-image generation using open-vocabulary scene control. In addition to a global text prompt that describes the entire scene, the user provides a segmentation map. We show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-conditional-based.
  arXiv  Detail & Related papers  (2022-11-25T18:59:10Z)
• Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models [0.0]
  We propose an optimization-free and zero fine-tuning framework that applies complex and non-rigid edits to a single real image via a text prompt. We prove our method's efficacy in producing high-quality, diverse, semantically coherent, and faithful real image edits.
  arXiv  Detail & Related papers  (2022-11-15T01:07:38Z)
• DiffEdit: Diffusion-based semantic image editing with mask guidance [64.555930158319]
  DiffEdit is a method to take advantage of text-conditioned diffusion models for the task of semantic image editing. Our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited.
  arXiv  Detail & Related papers  (2022-10-20T17:16:37Z)
• Prompt-to-Prompt Image Editing with Cross Attention Control [41.26939787978142]
  We present an intuitive prompt-to-prompt editing framework, where the edits are controlled by text only. We show our results over diverse images and prompts, demonstrating high-quality synthesis and fidelity to the edited prompts.
  arXiv  Detail & Related papers  (2022-08-02T17:55:41Z)
• FlexIT: Towards Flexible Semantic Image Translation [59.09398209706869]
  We propose FlexIT, a novel method which can take any input image and a user-defined text instruction for editing. First, FlexIT combines the input image and text into a single target point in the CLIP multimodal embedding space. We iteratively transform the input image toward the target point, ensuring coherence and quality with a variety of novel regularization terms.
  arXiv  Detail & Related papers  (2022-03-09T13:34:38Z)

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.