We present ContinuityCam, a novel approach to generate a continuous video
from a single static RGB image, using an event camera. Conventional cameras
struggle with high-speed motion capture due to bandwidth and dynamic range
limitations. Event cameras are ideal sensors to solve this problem because they
encode compressed change information at high temporal resolution. In this work,
we propose a novel task called event-based continuous color video
decompression, pairing single static color frames and events to reconstruct
temporally continuous videos. Our approach combines continuous long-range
motion modeling with a feature-plane-based synthesis neural integration model,
enabling frame prediction at arbitrary times within the events. Our method does
not rely on additional frames except for the initial image, increasing, thus,
the robustness to sudden light changes, minimizing the prediction latency, and
decreasing the bandwidth requirement. We introduce a novel single objective
beamsplitter setup that acquires aligned images and events and a novel and
challenging Event Extreme Decompression Dataset (E2D2) that tests the method in
various lighting and motion profiles. We thoroughly evaluate our method through
benchmarking reconstruction as well as various downstream tasks. Our approach
significantly outperforms the event- and image- based baselines in the proposed
task.

使用事件相机生成连续视频，通过与单一静态彩色图像配对的事件，结合连续的远距离运动建模和基于特征平面综合神经集成模型，实现任意时间帧的预测，从而高效地解决高速运动捕捉问题。