Framer: Interactive Frame Interpolation (2410.18978v2)

Abstract: We propose Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity. Concretely, besides taking the start and end frames as inputs, our approach supports customizing the transition process by tailoring the trajectory of some selected keypoints. Such a design enjoys two clear benefits. First, incorporating human interaction mitigates the issue arising from numerous possibilities of transforming one image to another, and in turn enables finer control of local motions. Second, as the most basic form of interaction, keypoints help establish the correspondence across frames, enhancing the model to handle challenging cases (e.g., objects on the start and end frames are of different shapes and styles). It is noteworthy that our system also offers an "autopilot" mode, where we introduce a module to estimate the keypoints and refine the trajectory automatically, to simplify the usage in practice. Extensive experimental results demonstrate the appealing performance of Framer on various applications, such as image morphing, time-lapse video generation, cartoon interpolation, etc. The code, the model, and the interface will be released to facilitate further research.

• The paper introduces an interactive framework that uses user-specified keypoint trajectories for precise control over video frame transitions.
• It leverages a pre-trained Stable Video Diffusion model with a dual conditioning mechanism to ensure coherent synthesis of intermediate frames.
• Experimental results demonstrate superior FVD scores and enhanced temporal consistency compared to traditional interpolation methods.

An Overview of "Framer: Interactive Frame Interpolation"

The paper "Framer: Interactive Frame Interpolation" introduces a novel framework designed to enhance video frame interpolation through user interaction. Unlike traditional methods that generate deterministic results primarily based on optical flow estimation, this approach allows for fine-grained control over frame transitions using customized keypoint trajectories. This interactive capability positions Framer as a flexible tool for various applications such as image morphing, slow-motion video generation, and cartoon interpolation.

Key Insights and Methodology

Framer leverages a pre-trained video diffusion model, specifically the Stable Video Diffusion (SVD), which provides a robust foundation for the synthesis of intermediate frames. This choice is instrumental due to SVD's established capacity for producing high-quality visual output. The authors introduce a dual conditioning mechanism to the latent space, whereby both the start and end frames are incorporated into the interpolation process, ensuring coherence in frame sequences.

The innovative aspect of Framer lies in its interactive module that uses keypoint trajectories as input. This module allows users to specify how objects within frames should move or transform, offering a hands-on approach to achieving desired interpolation outcomes. Framer also proposes an "autopilot" mode to automate trajectory estimation, addressing scenarios where manual input may be cumbersome.

Experimental Results

The experimental validation showcases Framer's ability to outperform existing methods, particularly in cases involving large motions or significant frame-to-frame changes. The paper details a series of experiments across multiple datasets, where the framework achieves superior FVD (Fréchet Video Distance) scores when compared to both traditional and diffusion-based interpolation techniques. The incorporation of keypoint guidance is highlighted as a critical factor in resolving ambiguities and enhancing temporal consistency.

Implications and Future Directions

The introduction of user interaction in video frame interpolation presents significant implications for practical applications. By providing users the ability to steer the interpolation process, Framer caters to creative industries looking for customizable animation tools, thereby expanding the potential use cases.

Theoretical implications include advancing the field of controllable video synthesis, which could inform future research in areas such as dynamic scene reconstruction and autonomous video editing. The next steps might involve enhancing the pre-trained models' robustness across diverse video types and exploring additional interactive control mechanisms beyond keypoint trajectories.

Conclusion

Framer represents a notable advancement in the domain of video frame interpolation by integrating user-centric controls into the process. While demonstrating compelling results and broad applicability, the framework also paves the way for future work in interactive video generation and synthesis. The authors disclose plans to release the model and codebase, which will likely foster further exploration and development in this area.