一次光流，任意时刻帧：面向事件相机视频帧插值的双向扭曲框架
One-Shot Flow, Any-Time Frame: A Bidirectional Warping Framework for Event-Based Video Frame Interpolation
提出一次性计算双向光流轨迹、任意时刻查询光流的框架，对事件相机视频插值和高效光流估计有直接参考价值，可复用于需要任意时刻运动表示的时序任务。
原文依据 Our framework uniquely computes a comprehensive motion trajectory representation in a single pass using a Bidirectional Flow Estimation Block (BiFEB)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视频帧插值（VFI）是视频处理中的一项关键任务。基于光流的方法尽管取得了成功，但受限于一个根本性困境：前向扭曲效率高但容易产生伪影，而后向扭曲质量更高但计算代价显著，尤其是在多帧插值时。这一权衡是一个主要瓶颈。为克服这一难题，我们提出了“一次光流，任意时刻帧”框架，这是一种面向事件相机视频帧插值（E-VFI）的新框架，能够在任意时刻插值时同时实现高效率和高质量。我们的框架利用事件数据的高时序分辨率，通过双向光流估计模块（BiFEB）以单次前向计算独特地构建全面的运动轨迹表示。随后，我们的光流查询（FQ）模块能够即时检索任意时间戳的双向光流，从而无需重复计算即可生成任意数量的帧。最后，一种新颖的双向扭曲（BiW）机制智能地融合了两种扭曲方向的优势，有效减轻伪影并产生高保真结果。大量实验表明，我们的方法在重建质量和推理效率上均持续超越最优的E-VFI方法，代表了高效高质量事件相机视频插值领域的实质性进展。*代码将在接收后发布。*
英文原文摘要 1455 字符
Video Frame Interpolation (VFI) is a crucial task in video processing. Flow-based methods, despite their success, are constrained by a fundamental dilemma: forward warping is efficient but prone to artifacts, while backward warping yields higher quality at a significant computational cost, especially for multi-frame interpolation. This trade-off is a major bottleneck. To overcome this, we introduce "One-Shot Flow, Any-Time Frame," a novel framework for Event-based VFI (E-VFI) that achieves both high efficiency and superior quality for arbitrary-time interpolation. Our framework uniquely computes a comprehensive motion trajectory representation in a single pass using a Bidirectional Flow Estimation Block (BiFEB), leveraging the high temporal resolution of event data. Subsequently, our Flow Query (FQ) module can instantly retrieve the bidirectional optical flow for any timestamp, enabling the generation of any number of frames without repeated computation. Finally, a novel Bidirectional Warping (BiW) mechanism intelligently fuses the strengths of both warping directions, effectively mitigating artifacts and producing high-fidelity results. Extensive experiments show that our approach consistently surpasses state-of-the-art E-VFI methods in both reconstruction quality and inference efficiency, representing a substantial advance in efficient and high-quality event-based video interpolation. *The code will be released after acceptance.*