From Sim-to-Real: Toward General Event-based Low-light Frame Interpolation with Per-scene Optimization
This work addresses low-light video frame interpolation for applications like video enhancement and slow-motion generation, but it is incremental as it builds on existing event-based methods with a tailored optimization approach.
The paper tackles the problem of event-based video frame interpolation in low-light conditions, which suffers from trailing artifacts and signal latency, by proposing a per-scene optimization strategy that uses internal sequence statistics to handle degraded event data, achieving state-of-the-art performance on a new low-light dataset.
Video Frame Interpolation (VFI) is important for video enhancement, frame rate up-conversion, and slow-motion generation. The introduction of event cameras, which capture per-pixel brightness changes asynchronously, has significantly enhanced VFI capabilities, particularly for high-speed, nonlinear motions. However, these event-based methods encounter challenges in low-light conditions, notably trailing artifacts and signal latency, which hinder their direct applicability and generalization. Addressing these issues, we propose a novel per-scene optimization strategy tailored for low-light conditions. This approach utilizes the internal statistics of a sequence to handle degraded event data under low-light conditions, improving the generalizability to different lighting and camera settings. To evaluate its robustness in low-light condition, we further introduce EVFI-LL, a unique RGB+Event dataset captured under low-light conditions. Our results demonstrate state-of-the-art performance in low-light environments. Project page: https://naturezhanghn.github.io/sim2real.