Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation (2303.00440v2)

Abstract: Effectively extracting inter-frame motion and appearance information is important for video frame interpolation (VFI). Previous works either extract both types of information in a mixed way or elaborate separate modules for each type of information, which lead to representation ambiguity and low efficiency. In this paper, we propose a novel module to explicitly extract motion and appearance information via a unifying operation. Specifically, we rethink the information process in inter-frame attention and reuse its attention map for both appearance feature enhancement and motion information extraction. Furthermore, for efficient VFI, our proposed module could be seamlessly integrated into a hybrid CNN and Transformer architecture. This hybrid pipeline can alleviate the computational complexity of inter-frame attention as well as preserve detailed low-level structure information. Experimental results demonstrate that, for both fixed- and arbitrary-timestep interpolation, our method achieves state-of-the-art performance on various datasets. Meanwhile, our approach enjoys a lighter computation overhead over models with close performance. The source code and models are available at https://github.com/MCG-NJU/EMA-VFI.

• The paper presents a novel inter-frame attention module that unifies motion and appearance extraction for improved video frame interpolation.
• It combines CNN and Transformer architectures to balance computational efficiency with detailed feature preservation, yielding higher PSNR and SSIM scores.
• The method reduces computational overhead and representation ambiguity, paving the way for enhanced video applications like slow-motion creation and novel-view rendering.

Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation

The paper, "Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation," puts forth an innovative approach to video frame interpolation (VFI) by addressing key inefficiencies in current methodologies. The authors identify the challenges in extracting inter-frame motion and appearance information, which are critical for achieving high fidelity in VFI tasks. Traditional methods either confuse these two types of information by handling them in a mixed manner or introduce computational overhead by designing separate modules, thereby leading to efficiency issues and representation ambiguity.

Methodology Overview

The paper introduces a novel module that leverages inter-frame attention to extract motion and appearance information in a unified manner. The core innovation lies in rethinking the information processing in inter-frame attention. The authors propose reusing the attention map for dual purposes: enhancing the appearance features and extracting motion information. Furthermore, the module is integrated into a hybrid pipeline combining Convolutional Neural Networks (CNNs) and Transformer architecture to strike a balance between computational efficiency and the preservation of detailed structural information.

Experimental Results and Comparison

Experiments conducted demonstrate the efficacy of the proposed method across various datasets, achieving state-of-the-art performance in both fixed- and arbitrary-timestep interpolation while maintaining a lower computational burden compared to models with similar performance. Notably, on datasets like Xiph and SNU-FILM, which feature large motion content, the proposed method shows marked improvements in PSNR and SSIM metrics.

Implications and Future Directions

The implications of this research are profound for the field of low-level vision tasks, particularly in applications such as video compression, slow-motion video creation, and novel-view rendering. The ability to extract and utilize motion and appearance in a streamlined manner reduces the complexity and enhances the performance of VFI systems. The hybrid CNN and Transformer design also paves the way for more efficient models that can operate under varied computational constraints.

Looking forward, the research suggests several avenues for further exploration. One critical area is extending the inter-frame attention mechanism to accommodate multiple frame inputs beyond consecutive pairs, potentially leveraging additional contextual information to further enhance interpolation accuracy. Moreover, the integration of this method into broader video processing systems could lead to performance gains in tasks requiring the synthesis of temporally coherent frames, such as action recognition.

This paper succeeds in marrying the nuanced extraction of video frame features with practical efficiency improvements, offering a robust foundation for future innovations in video frame interpolation and related applications.