ReVideo: Remake a Video with Motion and Content Control

• URL: http://arxiv.org/abs/2405.13865v1
• Date: Wed, 22 May 2024 17:46:08 GMT
• Title: ReVideo: Remake a Video with Motion and Content Control
• Authors: Chong Mou, Mingdeng Cao, Xintao Wang, Zhaoyang Zhang, Ying Shan, Jian Zhang,
• Abstract summary: We present a novel attempt to Remake a Video (VideoRe) which allows precise video editing in specific areas through the specification of both content and motion. VideoRe addresses a new task involving the coupling and training imbalance between content and motion control. Our method can also seamlessly extend these applications to multi-area editing without modifying specific training, demonstrating its flexibility and robustness.
• Score: 67.5923127902463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite significant advancements in video generation and editing using diffusion models, achieving accurate and localized video editing remains a substantial challenge. Additionally, most existing video editing methods primarily focus on altering visual content, with limited research dedicated to motion editing. In this paper, we present a novel attempt to Remake a Video (ReVideo) which stands out from existing methods by allowing precise video editing in specific areas through the specification of both content and motion. Content editing is facilitated by modifying the first frame, while the trajectory-based motion control offers an intuitive user interaction experience. ReVideo addresses a new task involving the coupling and training imbalance between content and motion control. To tackle this, we develop a three-stage training strategy that progressively decouples these two aspects from coarse to fine. Furthermore, we propose a spatiotemporal adaptive fusion module to integrate content and motion control across various sampling steps and spatial locations. Extensive experiments demonstrate that our ReVideo has promising performance on several accurate video editing applications, i.e., (1) locally changing video content while keeping the motion constant, (2) keeping content unchanged and customizing new motion trajectories, (3) modifying both content and motion trajectories. Our method can also seamlessly extend these applications to multi-area editing without specific training, demonstrating its flexibility and robustness.

Related papers

• Zero-Shot Video Editing through Adaptive Sliding Score Distillation [51.57440923362033]
  This study proposes a novel paradigm of video-based score distillation, facilitating direct manipulation of original video content. We propose an Adaptive Sliding Score Distillation strategy, which incorporates both global and local video guidance to reduce the impact of editing errors.
  arXiv  Detail & Related papers  (2024-06-07T12:33:59Z)
• MotionFollower: Editing Video Motion via Lightweight Score-Guided Diffusion [94.66090422753126]
  MotionFollower is a lightweight score-guided diffusion model for video motion editing. It delivers superior motion editing performance and exclusively supports large camera movements and actions. Compared with MotionEditor, the most advanced motion editing model, MotionFollower achieves an approximately 80% reduction in GPU memory.
  arXiv  Detail & Related papers  (2024-05-30T17:57:30Z)
• I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models [18.36472998650704]
  We introduce a novel and generic solution that extends the applicability of image editing tools to videos by propagating edits from a single frame to the entire video using a pre-trained image-to-video model. Our method, dubbed I2VEdit, adaptively preserves the visual and motion integrity of the source video depending on the extent of the edits.
  arXiv  Detail & Related papers  (2024-05-26T11:47:40Z)
• Edit-Your-Motion: Space-Time Diffusion Decoupling Learning for Video Motion Editing [46.56615725175025]
  We introduce Edit-Your-Motion, a video motion editing method that tackles unseen challenges through one-shot fine-tuning. To effectively decouple motion and appearance of source video, we design atemporal-two-stage learning strategy. With Edit-Your-Motion, users can edit the motion of humans in the source video, creating more engaging and diverse content.
  arXiv  Detail & Related papers  (2024-05-07T17:06:59Z)
• Action Reimagined: Text-to-Pose Video Editing for Dynamic Human Actions [49.14827857853878]
  ReimaginedAct comprises video understanding, reasoning, and editing modules. Our method can accept not only direct instructional text prompts but also what if' questions to predict possible action changes.
  arXiv  Detail & Related papers  (2024-03-11T22:46:46Z)
• SAVE: Protagonist Diversification with Structure Agnostic Video Editing [29.693364686494274]
  Previous works usually work well on trivial and consistent shapes, and easily collapse on a difficult target that has a largely different body shape from the original one. We propose motion personalization that isolates the motion from a single source video and then modifies the protagonist accordingly. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow.
  arXiv  Detail & Related papers  (2023-12-05T05:13:20Z)
• MotionEditor: Editing Video Motion via Content-Aware Diffusion [96.825431998349]
  MotionEditor is a diffusion model for video motion editing. It incorporates a novel content-aware motion adapter into ControlNet to capture temporal motion correspondence.
  arXiv  Detail & Related papers  (2023-11-30T18:59:33Z)
• Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models [65.268245109828]
  Ground-A-Video is a video-to-video translation framework for multi-attribute video editing. It attains temporally consistent editing of input videos in a training-free manner. Experiments and applications demonstrate that Ground-A-Video's zero-shot capacity outperforms other baseline methods in terms of edit-accuracy and frame consistency.
  arXiv  Detail & Related papers  (2023-10-02T11:28:37Z)

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.