A Unified Pyramid Recurrent Network for Video Frame Interpolation (2211.03456v2)

Abstract: Flow-guided synthesis provides a common framework for frame interpolation, where optical flow is estimated to guide the synthesis of intermediate frames between consecutive inputs. In this paper, we present UPR-Net, a novel Unified Pyramid Recurrent Network for frame interpolation. Cast in a flexible pyramid framework, UPR-Net exploits lightweight recurrent modules for both bi-directional flow estimation and intermediate frame synthesis. At each pyramid level, it leverages estimated bi-directional flow to generate forward-warped representations for frame synthesis; across pyramid levels, it enables iterative refinement for both optical flow and intermediate frame. In particular, we show that our iterative synthesis strategy can significantly improve the robustness of frame interpolation on large motion cases. Despite being extremely lightweight (1.7M parameters), our base version of UPR-Net achieves excellent performance on a large range of benchmarks. Code and trained models of our UPR-Net series are available at: https://github.com/srcn-ivl/UPR-Net.

• The paper introduces UPR-Net, which integrates bi-directional flow estimation and iterative frame synthesis to effectively handle large motion in video interpolation.
• It employs a coarse-to-fine pyramid structure that refines predictions across multiple levels, improving PSNR and SSIM scores on key benchmarks.
• Its lightweight architecture, with only 1.7 million parameters, enables efficient deployment in video streaming, gaming, and animation applications.

A Unified Pyramid Recurrent Network for Video Frame Interpolation

The paper presents UPR-Net, a Unified Pyramid Recurrent Network for video frame interpolation, a critical task in computer vision aimed at synthesizing intermediate frames to increase video frame rates. UPR-Net distinguishes itself by combining lightweight recurrent modules within a flexible pyramid framework to achieve competitive performance with a minimal number of parameters.

Key Contributions and Methodology

1. Bi-directional Flow Estimation and Frame Synthesis: UPR-Net employs recurrent modules to perform both bi-directional flow estimation and forward-warping based frame synthesis. The network iteratively refines both the optical flow and the intermediate frame across pyramid levels, which enhances robustness against large motion artifacts.
2. Iterative and Coarse-to-Fine Strategy: Traditional interpolation methods generally estimate optical flow from coarse to fine but perform frame synthesis only once at the final resolution. In contrast, UPR-Net synthesizes frames iteratively across multiple levels, significantly improving interpolation quality in the presence of large movements by continuously refining earlier estimates.
3. Lightweight Architecture: UPR-Net achieves strong performance despite having only 1.7 million parameters. This efficiency results from the integrated design that unites motion estimation and frame synthesis, allowing the model to be deployed on resource-constrained devices.
4. Resolution-aware Testing: UPR-Net can adapt to different resolutions at test time by altering the number of pyramid levels, specifically enabling effective handling of large motions in high-resolution videos.

Results and Evaluation

UPR-Net delivers state-of-the-art performance on several benchmarks, including UCF101, Vimeo90K, SNU-FILM, and 4K1000FPS. It is particularly noteworthy for its ability to interpolate video frames with minimal artifacts, even in challenging conditions involving substantial motion displacement. The performance prowess of UPR-Net was demonstrated through head-to-head comparisons with existing models, where it showed superior results in both PSNR and SSIM scores, especially for larger motion scenarios commonly encountered in high-frame-rate videos.

Implications and Future Directions

The implications of this research are considerable on both practical and theoretical fronts. In practice, UPR-Net's lightweight characteristic and its capability to generalize well to diverse video contents mark it as a viable option for real-world applications in video games, animation, and streaming services where efficient frame interpolation is crucial. Theoretically, the unified pyramid recurrent architecture presents a modular and scalable framework that can inspire further exploration into pyramid-based recurrent neural networks for tasks beyond frame interpolation.

Looking forward, opportunities exist to expand on UPR-Net by exploring its integration with existing state-of-the-art optical flow predictors and training on more extensive datasets for enhanced generalization. Further advances could include extending the methodology to accommodate even higher resolutions and optimizing the recurrent modules for refined efficiency.

In conclusion, UPR-Net advances the video frame interpolation domain by coupling a lightweight and adaptable architecture with robust performance against large motion, making it a compelling choice for future applications and research developments in video processing.