IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation (2205.14620v1)

Abstract: Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters or large delay, hindering them from diverse real-time applications. In this work, we devise an efficient encoder-decoder based network, termed IFRNet, for fast intermediate frame synthesizing. It first extracts pyramid features from given inputs, and then refines the bilateral intermediate flow fields together with a powerful intermediate feature until generating the desired output. The gradually refined intermediate feature can not only facilitate intermediate flow estimation, but also compensate for contextual details, making IFRNet do not need additional synthesis or refinement module. To fully release its potential, we further propose a novel task-oriented optical flow distillation loss to focus on learning the useful teacher knowledge towards frame synthesizing. Meanwhile, a new geometry consistency regularization term is imposed on the gradually refined intermediate features to keep better structure layout. Experiments on various benchmarks demonstrate the excellent performance and fast inference speed of proposed approaches. Code is available at https://github.com/ltkong218/IFRNet.

• The paper presents IFRNet, which integrates flow estimation and feature refinement into a single encoder-decoder network for efficient frame interpolation.
• It introduces novel task-oriented loss functions to enhance optical flow accuracy and maintain feature consistency during interpolation.
• Experimental results on benchmarks like Vimeo90K and Middlebury demonstrate IFRNet’s state-of-the-art performance and computational efficiency.

Intermediate Feature Refine Network for Efficient Frame Interpolation

The paper under discussion presents IFRNet, an innovative approach to video frame interpolation (VFI). The aim is to address the deficiencies of prevailing methods, which often feature complex architectures with heavy computational demands. IFRNet offers a compact, encoder-decoder-based solution that executes fast frame interpolation by deriving and refining intermediate features and optical flow in a unified model.

Technical Contributions

1. Unified Encoder-Decoder Architecture IFRNet integrates flow estimation and feature refinement into a single encoder-decoder structure. This design consolidates the necessary components for VFI, circumventing the need for separate synthesis networks or cascaded architectures, thus facilitating real-time applications. The encoder extracts pyramid features from input frames. A series of coarse-to-fine decoders then refine these features alongside the optical flow to yield the interpolated frame.
2. Task-Oriented Loss Functions Two novel loss formulations are introduced. The task-oriented flow distillation loss focuses on learning flow information that specifically benefits frame synthesis. By adjusting distillation based on spatial robustness, this loss ensures that only beneficial knowledge from a teacher model guides the flow prediction. Additionally, the feature space geometry consistency loss leverages the geometric properties of extracted pyramid features to maintain structure during feature refinement, ensuring that details are preserved without compromising the contextual richness needed for accurate interpolation.
3. Intermediate Feature Refinement The gradual refinement of intermediate features provides a dual benefit: it enhances flow estimation precision and helps capture contextual details effectively. This strategy supports sharper moving object boundaries and richer texture details in the synthesized frames, addressing common challenges like motion blur, occlusion, and lighting inconsistencies.

Evaluation and Performance

Empirical evaluation across diverse datasets, including Vimeo90K, UCF101, SNU-FILM, and Middlebury, underpins IFRNet's strong performance. The measured metrics, PSNR and SSIM, illustrate its proficiency. It notably achieves state-of-the-art accuracy on Vimeo90K and holds commendable rankings on the Middlebury benchmark, excelling in both IE (interpolation error) and NIE (normalized interpolation error) with significant efficiency gains over baselines.

Practical and Theoretical Implications

Practically, IFRNet enables high-quality frame interpolation for applications ranging from video playback enhancement to animation. Its lightweight and fast inference design make it suitable for deployment in resource-constrained environments like mobile devices. Theoretically, the paper challenges the current flow-based interpolation paradigms by demonstrating the effectiveness of a conjugated feature and flow refinement process in a unified model. This paves the way for further exploration into even more compact and efficient VFI models in the future.

Future Work

The approach opens several avenues for future research. Enhancements to the intermediate feature extraction could further reduce dependence on robust pre-trained models for pseudo-label generation, thus simplifying training pipelines. Moreover, exploring the adaptability of the model across varied video formats and real-world conditions can broaden its applicability. Finally, the fundamental principles of IFRNet may extend beyond video, influencing techniques in other temporal data processing tasks within the AI domain.