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. Both the dataset and the source code
will be made publicly available upon publication. Project page:
this https URL

提出一种针对低光照条件下事件相机的视频帧插值的优化策略，利用场景的内部统计信息来处理低光照条件下退化的事件数据，提高对不同光照和相机设置的泛化能力。

从模拟到真实：基于事件的通用低光帧插值与每场景优化

From Sim-to-Real: Toward General Event-based Low-light Frame  Interpolation with Per-scene Optimization

We present ZeroRF, a novel per-scene optimization method addressing the
challenge of sparse view 360{\deg} reconstruction in neural field
representations. Current breakthroughs like Neural Radiance Fields (NeRF) have
demonstrated high-fidelity image synthesis but struggle with sparse input
views. Existing methods, such as Generalizable NeRFs and per-scene optimization
approaches, face limitations in data dependency, computational cost, and
generalization across diverse scenarios. To overcome these challenges, we
propose ZeroRF, whose key idea is to integrate a tailored Deep Image Prior into
a factorized NeRF representation. Unlike traditional methods, ZeroRF
parametrizes feature grids with a neural network generator, enabling efficient
sparse view 360{\deg} reconstruction without any pretraining or additional
regularization. Extensive experiments showcase ZeroRF's versatility and
superiority in terms of both quality and speed, achieving state-of-the-art
results on benchmark datasets. ZeroRF's significance extends to applications in
3D content generation and editing. Project page:
this https URL

ZeroRF 是一种新颖的针对稀疏观测 360 度重构在神经场表示中的场景优化方法，通过在分解的 NeRF 表示中集成定制的深度图像先验，实现了高质量、高效率的稀疏观测 360 度重构，具有优于现有方法的性能。