Real-Time Intermediate Flow Estimation for Video Frame Interpolation (2011.06294v12)

Abstract: Real-time video frame interpolation (VFI) is very useful in video processing, media players, and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI method, RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed. A privileged distillation scheme is designed for stable IFNet training and improve the overall performance. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation with the temporal encoding input. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. Compared with the popular SuperSlomo and DAIN methods, RIFE is 4--27 times faster and produces better results. Furthermore, RIFE can be extended to wider applications thanks to temporal encoding. The code is available at https://github.com/megvii-research/ECCV2022-RIFE.

• The paper introduces IFNet, a novel architecture that refines flow estimates in a coarse-to-fine manner without costly operations.
• It leverages a privileged distillation scheme to stabilize training and eliminate reliance on pre-trained optical flow models.
• RIFE outperforms methods like SuperSlomo and DAIN, achieving significant speedups and improved video quality metrics such as PSNR and SSIM.

Real-Time Intermediate Flow Estimation for Video Frame Interpolation

The paper introduces RIFE, a novel algorithm focused on Real-time Intermediate Flow Estimation for Video Frame Interpolation (VFI). VFI aims to synthesize intermediate frames between consecutive video frames and is applicable in diverse domains, such as video editing, compression, and adaptive frame rate conversion. The primary challenge in VFI is handling complex, nonlinear motion and illumination changes in real-world videos.

Core Contributions

The authors propose IFNet, a neural network designed to estimate intermediate optical flows directly from input frames, prioritizing computation speed and quality. RIFE operates without relying on pre-trained optical flow models, enhancing its adaptability for various time step interpolations through temporal encoding.

Key contributions include:

1. IFNet Architecture: A coarse-to-fine design iteratively refines flow estimates across different resolutions, using lightweight IFBlocks. This design avoids typical costly operations like cost volumes, favoring simpler $3\times3$ convolutions and deconvolutions, making it suitable for devices with resource constraints.
2. Privileged Distillation Scheme: The training incorporates a teacher-student framework wherein the privileged teacher model, with access to the ground truth intermediate frame, guides the student model. This approach stabilizes training and accelerates convergence without relying on external optical flow ground truths, unlike alternative approaches that leverage pre-trained models.
3. State-of-the-Art Performance: RIFE achieves superior results on benchmarks such as Vimeo90K and HD, notably outperforming existing methods like SuperSlomo and DAIN by factors of 4 to 27 in speed, alongside producing better quality interpolations.

Experimental Analysis

The experimental results are robust across several datasets. RIFE consistently outperforms previous methods in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative aspects, addressing artifacts that competitors often produce.

Implications for Future Developments

The approach's reliance on direct flow estimation and temporal encoding showcases potential for advancements in VFI, particularly within diversified applications like depth map interpolation and dynamic scene stitching. The model's lightweight nature also suggests possibilities for on-device video processing, paving the way for low-latency, high-resolution video applications in consumer electronics.

Conclusion

By eliminating dependence on pre-trained models and reinforcing stability through a privileged distillation scheme, RIFE presents significant methodological progress in video frame interpolation. Its real-time performance with high-quality output marks a notable stride in VFI research, promising consequential impacts on both theoretical development and practical implementations in the field of artificial intelligence and computer vision. Future work could explore extending the model to multi-frame inputs and enhancing perceptual quality metrics, opening new avenues for innovation in video processing technologies.