MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing

• URL: http://arxiv.org/abs/2311.17338v3
• Date: Mon, 15 Jul 2024 08:51:43 GMT
• Title: MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing
• Authors: Haoyu Zhao, Tianyi Lu, Jiaxi Gu, Xing Zhang, Qingping Zheng, Zuxuan Wu, Hang Xu, Yu-Gang Jiang,
• Abstract summary: We propose a unified Multi-alignment Diffusion, dubbed as MagDiff, for both tasks of high-fidelity video generation and editing. The proposed MagDiff introduces three types of alignments, including subject-driven alignment, adaptive prompts alignment, and high-fidelity alignment.
• Score: 90.06041718086317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The diffusion model is widely leveraged for either video generation or video editing. As each field has its task-specific problems, it is difficult to merely develop a single diffusion for completing both tasks simultaneously. Video diffusion sorely relying on the text prompt can be adapted to unify the two tasks. However, it lacks a high capability of aligning heterogeneous modalities between text and image, leading to various misalignment problems. In this work, we are the first to propose a unified Multi-alignment Diffusion, dubbed as MagDiff, for both tasks of high-fidelity video generation and editing. The proposed MagDiff introduces three types of alignments, including subject-driven alignment, adaptive prompts alignment, and high-fidelity alignment. Particularly, the subject-driven alignment is put forward to trade off the image and text prompts, serving as a unified foundation generative model for both tasks. The adaptive prompts alignment is introduced to emphasize different strengths of homogeneous and heterogeneous alignments by assigning different values of weights to the image and the text prompts. The high-fidelity alignment is developed to further enhance the fidelity of both video generation and editing by taking the subject image as an additional model input. Experimental results on four benchmarks suggest that our method outperforms the previous method on each task.

Related papers

• 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)
• MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance [6.4680449907623006]
  This research introduces the MS-Diffusion framework for layout-guided zero-shot image personalization with multi-subjects. The proposed multi-subject cross-attention orchestrates inter-subject compositions while preserving the control of texts.
  arXiv  Detail & Related papers  (2024-06-11T12:32:53Z)
• Contextualized Diffusion Models for Text-Guided Image and Video Generation [67.69171154637172]
  Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. We propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample. We generalize our model to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing.
  arXiv  Detail & Related papers  (2024-02-26T15:01:16Z)
• Consolidating Attention Features for Multi-view Image Editing [126.19731971010475]
  We focus on spatial control-based geometric manipulations and introduce a method to consolidate the editing process across various views. We introduce QNeRF, a neural radiance field trained on the internal query features of the edited images. We refine the process through a progressive, iterative method that better consolidates queries across the diffusion timesteps.
  arXiv  Detail & Related papers  (2024-02-22T18:50:18Z)
• DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing [66.43179841884098]
  Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. We propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing. Our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks.
  arXiv  Detail & Related papers  (2024-02-04T18:50:29Z)
• Unified Diffusion-Based Rigid and Non-Rigid Editing with Text and Image Guidance [15.130419159003816]
  We present a versatile image editing framework capable of executing both rigid and non-rigid edits. We leverage a dual-path injection scheme to handle diverse editing scenarios. We introduce an integrated self-attention mechanism for fusion of appearance and structural information.
  arXiv  Detail & Related papers  (2024-01-04T08:21:30Z)
• InstructVid2Vid: Controllable Video Editing with Natural Language Instructions [97.17047888215284]
  InstructVid2Vid is an end-to-end diffusion-based methodology for video editing guided by human language instructions. Our approach empowers video manipulation guided by natural language directives, eliminating the need for per-example fine-tuning or inversion.
  arXiv  Detail & Related papers  (2023-05-21T03:28:13Z)
• Pix2Video: Video Editing using Image Diffusion [43.07444438561277]
  We investigate how to use pre-trained image models for text-guided video editing. Our method works in two simple steps: first, we use a pre-trained structure-guided (e.g., depth) image diffusion model to perform text-guided edits on an anchor frame. We demonstrate that realistic text-guided video edits are possible, without any compute-intensive preprocessing or video-specific finetuning.
  arXiv  Detail & Related papers  (2023-03-22T16:36:10Z)

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.