Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 60 tok/s
Gemini 2.5 Pro 46 tok/s Pro
GPT-5 Medium 23 tok/s Pro
GPT-5 High 30 tok/s Pro
GPT-4o 87 tok/s Pro
Kimi K2 194 tok/s Pro
GPT OSS 120B 460 tok/s Pro
Claude Sonnet 4.5 28 tok/s Pro
2000 character limit reached

VideoHandles: Editing 3D Object Compositions in Videos Using Video Generative Priors (2503.01107v2)

Published 3 Mar 2025 in cs.CV

Abstract: Generative methods for image and video editing use generative models as priors to perform edits despite incomplete information, such as changing the composition of 3D objects shown in a single image. Recent methods have shown promising composition editing results in the image setting, but in the video setting, editing methods have focused on editing object's appearance and motion, or camera motion, and as a result, methods to edit object composition in videos are still missing. We propose \name as a method for editing 3D object compositions in videos of static scenes with camera motion. Our approach allows editing the 3D position of a 3D object across all frames of a video in a temporally consistent manner. This is achieved by lifting intermediate features of a generative model to a 3D reconstruction that is shared between all frames, editing the reconstruction, and projecting the features on the edited reconstruction back to each frame. To the best of our knowledge, this is the first generative approach to edit object compositions in videos. Our approach is simple and training-free, while outperforming state-of-the-art image editing baselines.

Summary

Overview of VideoHandles: Editing 3D Object Compositions in Videos Using Generative Priors

The paper "VideoHandles: Editing 3D Object Compositions in Videos Using Video Generative Priors" introduces a novel framework for editing 3D object compositions in videos, extending the capabilities of previous image editing methodologies into the video domain. VideoHandles utilizes video generative priors to modify the 3D spatial positioning of objects in static scene videos, achieving consistency across frames. This approach addresses several critical challenges, such as maintaining temporal coherence and the realistic synthesis of changes in lighting and reflections due to object manipulation.

The paper's primary contribution lies in leveraging pretrained video generative models to guide edits in a temporally consistent manner. This is achieved by projecting latent features of the generative model into a shared 3D reconstruction space. From this shared space, edits can be applied in a coherent fashion across all video frames. Notably, the proposed method operates without the need for additional training or fine-tuning, contrasting with several existing approaches that require modifying or augmenting the base generative model.

Key Methodologies

The method involves several key steps:

  1. 3D Reconstruction: The approach begins by reconstructing a 3D point cloud of the scene from the video frames, addressing the task of estimating the camera pose and scene structure.
  2. Feature Projection: Intermediate features from a generative video model are projected onto this 3D reconstruction. These features are treated as latent textures, facilitating the consistent application of edits throughout the video.
  3. Editing Process: Users can define transformations (such as translations or rotations) within this 3D space, which are then applied to the corresponding object features across all frames.
  4. Warping and Guidance: A warping function ensures that spatial edits maintain coherence. VideoHandles uses a guided generative process where these warped features aid in reconstructing each frame, preserving temporal consistency and realistic appearance.
  5. Null-Text Prediction: To prevent duplication of objects in their original positions, the authors employ a null-text prediction strategy. This effectively minimizes the influence of text guidance over regions unnecessary for the new object positioning.

Evaluation and Comparison

The paper benchmarks VideoHandles against several baselines, including state-of-the-art image editing techniques. Its results highlight significant user preference for VideoHandles regarding plausibility and consistency. The method also excels in identity preservation and coherence of edits, supported by structured user studies and quantitative metrics like Frame LPIPS. Comparisons with frame-by-frame adjusted baselines, such as Diffusion Handles, underscore the advantages of utilizing unified video-model guidance.

Implications and Future Directions

VideoHandles marks a substantial advancement in video editing by enabling 3D object manipulation with temporal and spatial coherence, previously unaddressed in video generative modeling. Its training-free nature and use of video priors open new possibilities for dynamic content creation, automated video enhancement, and AR/VR applications.

However, the approach is constrained by the current quality of video generative models and static scene assumptions. Future work could explore dynamic scene adaptation and more refined 3D reconstruction methods, potentially integrating these into broader video synthesis tasks, including interactive video content creation and real-time editing solutions.

In summary, VideoHandles presents a solid framework for video composition editing, handling complex tasks such as shadow adjustment and reflection management, while maintaining object identity—a promising direction for generative video editing methodologies.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 3 posts and received 118 likes.

Youtube Logo Streamline Icon: https://streamlinehq.com