PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor

• URL: http://arxiv.org/abs/2303.17546v3
• Date: Mon, 8 Apr 2024 16:49:16 GMT
• Title: PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
• Authors: Vidit Goel, Elia Peruzzo, Yifan Jiang, Dejia Xu, Xingqian Xu, Nicu Sebe, Trevor Darrell, Zhangyang Wang, Humphrey Shi,
• Abstract summary: PAIR Diffusion is a generic framework that enables a diffusion model to control the structure and appearance of each object in the image. We show that having control over the properties of each object in an image leads to comprehensive editing capabilities. Our framework allows for various object-level editing operations on real images such as reference image-based appearance editing, free-form shape editing, adding objects, and variations.
• Score: 135.17302411419834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text, while others use low-level conditioning. Nevertheless, most of them lack fine-grained control over the properties of the different objects present in the image, i.e. object-level image editing. In this work, we tackle the task by perceiving the images as an amalgamation of various objects and aim to control the properties of each object in a fine-grained manner. Out of these properties, we identify structure and appearance as the most intuitive to understand and useful for editing purposes. We propose PAIR Diffusion, a generic framework that can enable a diffusion model to control the structure and appearance properties of each object in the image. We show that having control over the properties of each object in an image leads to comprehensive editing capabilities. Our framework allows for various object-level editing operations on real images such as reference image-based appearance editing, free-form shape editing, adding objects, and variations. Thanks to our design, we do not require any inversion step. Additionally, we propose multimodal classifier-free guidance which enables editing images using both reference images and text when using our approach with foundational diffusion models. We validate the above claims by extensively evaluating our framework on both unconditional and foundational diffusion models. Please refer to https://vidit98.github.io/publication/conference-paper/pair_diff.html for code and model release.

Related papers

• DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting [63.01425442236011]
  We present DreamMix, a diffusion-based generative model adept at inserting target objects into scenes at user-specified locations. We propose an Attribute Decoupling Mechanism (ADM) and a Textual Attribute Substitution (TAS) module to improve the diversity and discriminative capability of the text-based attribute guidance.
  arXiv  Detail & Related papers  (2024-11-26T08:44:47Z)
• SGEdit: Bridging LLM with Text2Image Generative Model for Scene Graph-based Image Editing [42.23117201457898]
  We introduce a new framework that integrates large language model (LLM) with Text2 generative model for graph-based image editing. Our framework significantly outperforms existing image editing methods in terms of editing precision and scene aesthetics.
  arXiv  Detail & Related papers  (2024-10-15T17:40:48Z)
• Achieving Complex Image Edits via Function Aggregation with Diffusion Models [15.509233098264513]
  Diffusion models have demonstrated strong performance in generative tasks, making them ideal candidates for image editing. We introduce FunEditor, an efficient diffusion model designed to learn atomic editing functions and perform complex edits by aggregating simpler functions. FunEditor is 5 to 24 times faster inference than existing methods on complex tasks like object movement.
  arXiv  Detail & Related papers  (2024-08-16T02:33:55Z)
• A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models [117.77807994397784]
  Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models. T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs.
  arXiv  Detail & Related papers  (2024-06-20T17:58:52Z)
• Editable Image Elements for Controllable Synthesis [79.58148778509769]
  We propose an image representation that promotes spatial editing of input images using a diffusion model. We show the effectiveness of our representation on various image editing tasks, such as object resizing, rearrangement, dragging, de-occlusion, removal, variation, and image composition.
  arXiv  Detail & Related papers  (2024-04-24T17:59:11Z)
• LoMOE: Localized Multi-Object Editing via Multi-Diffusion [8.90467024388923]
  We introduce a novel framework for zero-shot localized multi-object editing through a multi-diffusion process. Our approach leverages foreground masks and corresponding simple text prompts that exert localized influences on the target regions. A combination of cross-attention and background losses within the latent space ensures that the characteristics of the object being edited are preserved.
  arXiv  Detail & Related papers  (2024-03-01T10:46:47Z)
• VASE: Object-Centric Appearance and Shape Manipulation of Real Videos [108.60416277357712]
  In this work, we introduce a framework that is object-centric and is designed to control both the object's appearance and, notably, to execute precise and explicit structural modifications on the object. We build our framework on a pre-trained image-conditioned diffusion model, integrate layers to handle the temporal dimension, and propose training strategies and architectural modifications to enable shape control. We evaluate our method on the image-driven video editing task showing similar performance to the state-of-the-art, and showcasing novel shape-editing capabilities.
  arXiv  Detail & Related papers  (2024-01-04T18:59:24Z)
• DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models [66.43179841884098]
  We propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Our method achieves various editing modes for the generated or real images, such as object moving, object resizing, object appearance replacement, and content dragging.
  arXiv  Detail & Related papers  (2023-07-05T16:43:56Z)
• 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)

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.