Learning Event-guided Exposure-agnostic Video Frame Interpolation via Adaptive Feature Blending

TL;DR

This paper introduces a novel event-guided framework for exposure-agnostic video frame interpolation that effectively handles blurry, low-frame-rate videos captured under unknown exposure conditions by adaptively sampling and blending features.

Contribution

The paper proposes a new framework with Target-adaptive Event Sampling and Importance Mapping to improve VFI on exposure-agnostic videos, addressing limitations of previous event-guided methods.

Findings

Outperforms existing methods on synthetic datasets

Effective on real-world exposure-agnostic videos

Improves sharpness and temporal consistency

Abstract

Exposure-agnostic video frame interpolation (VFI) is a challenging task that aims to recover sharp, high-frame-rate videos from blurry, low-frame-rate inputs captured under unknown and dynamic exposure conditions. Event cameras are sensors with high temporal resolution, making them especially advantageous for this task. However, existing event-guided methods struggle to produce satisfactory results on severely low-frame-rate blurry videos due to the lack of temporal constraints. In this paper, we introduce a novel event-guided framework for exposure-agnostic VFI, addressing this limitation through two key components: a Target-adaptive Event Sampling (TES) and a Target-adaptive Importance Mapping (TIM). Specifically, TES samples events around the target timestamp and the unknown exposure time to better align them with the corresponding blurry frames. TIM then generates an importance map that considers the temporal proximity and spatial relevance of consecutive features to the target. Guided by this map, our framework adaptively blends consecutive features, allowing temporally aligned features to serve as the primary cues while spatially relevant ones offer complementary support. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our approach in exposure-agnostic VFI scenarios.