Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields (2502.13716v1)

Abstract: Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, existing methods estimate bidirectional inter-frame motion fields with only events or approximations, which can not consider the complex motion in real-world scenarios. In this paper, we propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. In detail, our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. Moreover, we develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features. Finally, we build a large-scale event-based VFI dataset, ERF-X170FPS, with a high frame rate, extreme motion, and dynamic textures to overcome the limitations of previous event-based VFI datasets. Extensive experimental results validate that our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets. Our project pages are available at: https://github.com/intelpro/CBMNet

• The paper introduces EIF-BiOFNet, a novel network that estimates asymmetric bidirectional motion fields by fusing event and image data, improving video frame interpolation without motion approximation.
• The research contributes the ERF-X170FPS dataset, providing high-resolution, high-frame-rate sequences with diverse motion to rigorously evaluate VFI methods.
• Performance evaluations show the proposed method significantly improves interpolation accuracy, especially in complex motion scenarios, enhancing applications like dynamic scene reconstruction and autonomous navigation.

Analysis of Event-Based Video Frame Interpolation Using Cross-Modal Asymmetric Bidirectional Motion Fields

The paper, "Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields," presents a novel approach to video frame interpolation (VFI) by leveraging the high temporal resolution of event cameras. Event cameras, inspired by biological vision, primarily record changes in brightness, enabling them to capture minute motion details with microsecond precision. Traditional VFI methods usually rely on optical frames to estimate bidirectional motion fields, often employing linear or quadratic approximations. However, these methods struggle with complex or non-linear motion scenarios, which are common in real-world settings. This paper introduces an innovative framework that combines the strengths of event cameras and conventional imagers to improve motion field estimation without approximation, thereby enhancing interpolation performance.

Critical Contributions

1. Cross-Modal Motion Field Estimation: The paper's cornerstone is the EIF-BiOFNet, a network designed to estimate asymmetric bidirectional motion fields by fusing event and image data. This approach effectively exploits both the temporal precision of events and the spatial density of images to accurately model motion fields.
2. Interactive Attention-Based Frame Synthesis: Beyond motion estimation, the research proposes an overview method that combines warping-based and synthesis-based features using an interactive attention mechanism, improving the utilization of long-range pixel correlations inherent in complex scenes.
3. Introduction of High-Quality Dataset: The paper contributes a new dataset, ERF-X170FPS, which presents high-resolution, high-frame-rate sequences with diverse motion patterns and dynamic textures. This dataset is designed to address the limitations of existing benchmarks, providing a more rigorous test bed for evaluating VFI methods.

Performance Evaluation

The proposed methodology is comprehensively validated against state-of-the-art VFI techniques over various synthetic and real-world datasets. The results reveal substantial improvements in interpolation accuracy, reflected in notable PSNR gains and qualitative assessments, especially in scenarios involving significant motion complexity. The ability of the EIF-BiOFNet to eschew motion approximation allows it to maintain robustness across challenging interpolation tasks.

Implications and Future Directions

This research signifies an important step towards more accurate VFI methods that can handle real-world complexities. Practically, it enhances the feasibility of deploying event-based systems in domains like dynamic scene reconstruction, autonomous navigation, and content creation in media production.

The integration of event-driven data with traditional imaging opens multiple avenues for further exploration in artificial intelligence applications. Future investigations could explore optimizing the network's computational cost, making it more accessible for real-time applications. Additionally, exploring the integration of this framework within end-to-end systems for other vision applications could yield significant advancements.

In conclusion, this paper introduces a robust solution to the longstanding challenges in video frame interpolation posed by complex motion, demonstrating the untapped potential of event-based sensors when used in synergy with conventional imaging techniques.