AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation (2304.09790v1)

Abstract: We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately. Combining these two designs enables us to generate promising task-oriented flows and reduce the difficulties in modeling large motions and handling occluded areas during frame interpolation. These qualities promote our model to achieve state-of-the-art performance on various benchmarks with high efficiency. Moreover, our convolution-based model competes favorably compared to Transformer-based models in terms of accuracy and efficiency. Our code is available at https://github.com/MCG-NKU/AMT.

• The paper introduces a novel AMT framework that builds bidirectional correlation volumes to model dense pixel correspondences for enhanced video frame interpolation.
• The paper designs a multi-field refinement process that derives finer flow fields from coarse flows, effectively addressing occlusions and large motions.
• The paper demonstrates superior PSNR and SSIM scores on benchmarks like Vimeo90K and UCF101 while significantly reducing computational costs.

An Academic Overview of "AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation"

The paper introduces All-Pairs Multi-Field Transforms (AMT), a notable contribution in the field of video frame interpolation (VFI). The focus of this research is to enhance the temporal resolution of input video by synthesizing intermediate frames through a novel network architecture. The authors address key challenges in VFI, such as modeling large motions and handling occlusions, by proposing two core designs within the AMT architecture.

Core Contributions and Methodology

The first innovation involves the construction of bidirectional correlation volumes for all pairs of pixels. This method is inspired by the RAFT flow estimation model and improves upon it by ensuring that dense correspondences between input frames are accurately modeled, even for large displacements. The second major contribution is the use of multi-field refinement. This step generates multiple groups of fine-grained flow fields from a pair of coarse flows, enhancing the ability to handle occlusions and refine flow details effectively.

The AMT framework is organized into several stages:

1. Feature Extraction: Introduction of two separate encoders for correlation and context feature extraction. This setup allows for the initial prediction of bilateral flows and intermediate content features, crucial for subsequent updates and refinement.
2. Bidirectional Correlation Volumes: Establishment of a correlation structure that captures forward and backward flow dynamics, unlike the unidirectional approach in RAFT.
3. Scaled Correlation Lookup: Innovative approach to align the coordinate systems of correlation volumes and the flows of the invisible intermediate frame, a crucial step in ensuring accurate updates in the flow prediction process.
4. Cross-Scale Updates: AMT performs updates in a coarse-to-fine manner, progressively refining both the flow fields and the interpolated features.
5. Multi-Field Refinement: From updated coarse flows, multiple finer flow fields are derived in a single network pass. These are jointly used with occlusion masks and residual content to generate plausible intermediate frames by backward warping.

Results and Comparison

AMT demonstrates state-of-the-art performance across multiple benchmarks, including Vimeo90K, UCF101, and SNU-FILM, consistently outperforming recent methods such as IFRNet and transformer-based models like VFIFormer. Specifically, the network achieves superior PSNR and SSIM scores while maintaining computational efficiency, a haLLMark of practical importance. AMT-S shows a clear advantage over its competitors by achieving better accuracy with fewer parameters and computational costs.

Implications and Future Directions

The introduction of all-pairs multi-field transforms signifies an advancement in efficiently bridging the gap between flow estimation and task-specific flow accuracy. It highlights the advantages of cross-scale back-and-forth modeling and demonstrates the power of task-oriented architecture design in improving the fidelity and generality of the VFI process.

Looking forward, the scalability of the AMT architecture might open avenues for further applications in high-resolution video processing. Improving efficiency and scalability could involve exploring alternative designs for correlation volume computation and refinement, especially at higher image resolutions. Moreover, exploring the integration of AMT with other video processing tasks could provide a unified framework, enhancing its applicability in varied contexts like video enhancement, compression, and novel view synthesis.

In conclusion, the paper presents a significant methodological contribution to the VFI domain, positing AMT as not only a state-of-the-art performer but also a model for future innovations in efficiently handling complex and large motion scenarios in video processing.