GIMM-VFI introduces a groundbreaking approach to video frame interpolation by utilizing generalizable implicit motion modeling. It enables smooth transitions between adjacent frames at any arbitrary time step, enhancing video quality and visual experience. Discover advanced motion modeling techniques that redefine how we perceive motion in videos.
GIMM-VFI: Generalizable Implicit Motion Modeling for Video Frame Interpolation
Developed by Zujin Guo, Wei Li, and Chen Change Loy from S-Lab at Nanyang Technological University, GIMM-VFI offers a cutting-edge approach to video frame interpolation through advanced motion modeling techniques. Featured at NeurIPS 2024, this project represents a significant leap forward in generating high-quality interpolated frames between adjacent video sequences.
Key Features
- Continuous Motion Modeling: GIMM-VFI excels in generating interpolated frames at arbitrary timesteps, ensuring seamless video transitions and enhanced visual quality.
- Versatile Implementation: The repository supports different flow estimators, including:
- RAFT-based GIMM-VFI-R
- FlowFormer-based GIMM-VFI-F
- Perceptually Enhanced Versions: The perceptually enhanced models, GIMM-VFI-R-P and GIMM-VFI-F-P, utilize an additional learning objective—LPIPS loss—during training, yielding substantial improvements in visual perception during interpolation tasks.
Usage Example
To create interpolations using GIMM-VFI, you can execute the following command:
sh scripts/video_Nx.sh YOUR_PATH_TO_FRAME YOUR_OUTPUT_PATH DS_SCALE N_INTERP
For instance, for a 9X interpolation:
sh scripts/video_Nx.sh demo/input_frames demo/output 1 9
Datasets
GIMM-VFI incorporates several prominent datasets to refine its predictive capabilities, including:
For models and checkpoints, visit the Hugging Face Model Hub.
Conclusion
GIMM-VFI stands at the forefront of video frame interpolation technology, empowered by its generalizable motion modeling capabilities. Whether you are a researcher, video editor, or enthusiast, GIMM-VFI provides the tools and insights needed to advance your projects and enhance your visual content. For more details and visual demonstrations, visit the project page.