MotionV2V: Editing Motion in a Video

• URL: http://arxiv.org/abs/2511.20640v1
• Date: Tue, 25 Nov 2025 18:57:25 GMT
• Title: MotionV2V: Editing Motion in a Video
• Authors: Ryan Burgert, Charles Herrmann, Forrester Cole, Michael S Ryoo, Neal Wadhwa, Andrey Voynov, Nataniel Ruiz,
• Abstract summary: We propose modifying video motion by editing sparse trajectories extracted from the input.<n>We term the deviation between input and output trajectories a "motion edit"<n>Our approach allows for edits that start at any timestamp and propagate naturally.
• Score: 53.791975554391534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While generative video models have achieved remarkable fidelity and consistency, applying these capabilities to video editing remains a complex challenge. Recent research has explored motion controllability as a means to enhance text-to-video generation or image animation; however, we identify precise motion control as a promising yet under-explored paradigm for editing existing videos. In this work, we propose modifying video motion by directly editing sparse trajectories extracted from the input. We term the deviation between input and output trajectories a "motion edit" and demonstrate that this representation, when coupled with a generative backbone, enables powerful video editing capabilities. To achieve this, we introduce a pipeline for generating "motion counterfactuals", video pairs that share identical content but distinct motion, and we fine-tune a motion-conditioned video diffusion architecture on this dataset. Our approach allows for edits that start at any timestamp and propagate naturally. In a four-way head-to-head user study, our model achieves over 65 percent preference against prior work. Please see our project page: https://ryanndagreat.github.io/MotionV2V

Related papers

• MotionEdit: Benchmarking and Learning Motion-Centric Image Editing [81.28392925790568]
  We introduce MotionEdit, a novel dataset for motion-centric image editing.<n>MotionEdit provides high-fidelity image pairs depicting realistic motion transformations extracted from continuous videos.<n>We propose MotionNFT to compute motion alignment rewards based on how well the motion flow between input and model-edited images matches the ground-truth motion.
  arXiv  Detail & Related papers  (2025-12-11T04:53:58Z)
• Generative Video Motion Editing with 3D Point Tracks [66.55707897151909]
  We present a track-conditioned V2V framework that enables joint editing of camera and object motion.<n>We achieve this by conditioning a model on a source video and paired 3D point tracks representing source and target motions.<n>Our model supports diverse motion edits, including joint camera/object manipulation, motion transfer, and non-rigid deformation.
  arXiv  Detail & Related papers  (2025-12-01T18:59:55Z)
• Point-to-Point: Sparse Motion Guidance for Controllable Video Editing [29.888408281118846]
  We propose a novel motion representation, anchor tokens, that capture the most essential motion patterns by leveraging the rich prior of a video diffusion model.<n>In experiments, anchor tokens lead to more controllable and semantically aligned video edits, achieving superior performance in terms of edit and motion fidelity.
  arXiv  Detail & Related papers  (2025-11-23T03:59:59Z)
• Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy [48.72918598961146]
  We present Shape-for-Motion, a novel framework that incorporates a 3D proxy for precise and consistent video editing.<n>Our framework supports various precise and physically-consistent manipulations across the video frames, including pose editing, rotation, scaling, translation, texture modification, and object composition.
  arXiv  Detail & Related papers  (2025-06-27T17:59:01Z)
• Edit as You See: Image-guided Video Editing via Masked Motion Modeling [18.89936405508778]
  We propose a novel Image-guided Video Editing Diffusion model, termed IVEDiff.<n>IVEDiff is built on top of image editing models, and is equipped with learnable motion modules to maintain the temporal consistency of edited video.<n>Our method is able to generate temporally smooth edited videos while robustly dealing with various editing objects with high quality.
  arXiv  Detail & Related papers  (2025-01-08T07:52:12Z)
• 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)
• ReVideo: Remake a Video with Motion and Content Control [67.5923127902463]
  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.
  arXiv  Detail & Related papers  (2024-05-22T17:46:08Z)
• MagicStick: Controllable Video Editing via Control Handle Transformations [49.29608051543133]
  MagicStick is a controllable video editing method that edits the video properties by utilizing the transformation on the extracted internal control signals. We present experiments on numerous examples within our unified framework. We also compare with shape-aware text-based editing and handcrafted motion video generation, demonstrating our superior temporal consistency and editing capability than previous works.
  arXiv  Detail & Related papers  (2023-12-05T17:58:06Z)
• 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)

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.