Occlusion and motion blur make it challenging to interpolate video frame, since estimating complex motions between two frames is hard and unreliable, especially in highly dynamic scenes. This paper aims to address these issues by exploiting spike stream as auxiliary visual information between frames to synthesize target frames. Instead of estimating motions by optical flow from RGB frames, we present a new dual-modal pipeline adopting both RGB frames and the corresponding spike stream as inputs (SVFI). It extracts the scene structure and objects' outline feature maps of the target frames from spike stream. Those feature maps are fused with the color and texture feature maps extracted from RGB frames to synthesize target frames. Benefited by the spike stream that contains consecutive information between two frames, SVFI can directly extract the information in occlusion and motion blur areas of target frames from spike stream, thus it is more robust than previous optical flow-based methods. Experiments show SVFI outperforms the SOTA methods on wide variety of datasets. For instance, in 7 and 15 frame skip evaluations, it shows up to 5.58 dB and 6.56 dB improvements in terms of PSNR over the corresponding second best methods BMBC and DAIN. SVFI also shows visually impressive performance in real-world scenes.

Efficiently selecting an appropriate spike stream data length to extract precise information is the key to the spike vision tasks. To address this issue, we propose a dynamic timing representation for spike streams. Based on multi-layers architecture, it applies dilated convolutions on temporal dimension to extract features on multi-temporal scales with few parameters. And we design layer attention to dynamically fuse these features. Moreover, we propose an unsupervised learning method for optical flow estimation in a spike-based manner to break the dependence on labeled data. In addition, to verify the robustness, we also build a spike-based synthetic validation dataset for extreme scenarios in autonomous driving, denoted as SSES dataset. It consists of various corner cases. Experiments show that our method can predict optical flow from spike streams in different high-speed scenes, including real scenes. For instance, our method achieves $15\%$ and $19\%$ error reduction on PHM dataset compared to the best spike-based work, SCFlow, in $\Delta t=10$ and $\Delta t=20$ respectively, using the same settings as in previous works. The source code and dataset are available at \href{https://github.com/Bosserhead/USFlow}{https://github.com/Bosserhead/USFlow}.