Continuous Space-Time Video Super-Resolution via Event Camera

ICLR 2025 Conference Submission1238 Authors

17 Sept 2024 (modified: 28 Nov 2024)ICLR 2025 Conference SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Event Camera, Video Super-resolution, Video Frame Interpolation, Continuous Space-time Video Super-resolution
Abstract: Continuous space-time video super-resolution (C-STVSR) aims to simultaneously enhance video resolution and frame rate at an arbitrary scale. Recently, implicit neural representation (INR) has been applied to video restoration, representing videos as implicit fields that can be decoded at an arbitrary scale. However, the highly ill-posed nature of C-STVSR limits the effectiveness of current INR-based methods: they assume linear motion between frames and use interpolation or feature warping to generate features at arbitrary spatiotemporal positions with \ubtxt{two} consecutive frames. This restrains C-STVSR from capturing rapid and \ubtxt{nonlinear motion} and \ubtxt{long-term dependencies} (\textit{involving more than two frames}) in complex dynamic scenes. In this paper, we propose a novel C-STVSR framework, called \textbf{HR-INR}, which captures both \textbf{h}olistic dependencies and \textbf{r}egional motions based on INR. It is assisted by an event camera -- a novel sensor renowned for its high temporal resolution and low latency. To fully utilize the rich temporal information from events, we design a feature extraction consisting of (1) a regional event feature extractor -- taking events as inputs via the proposed event temporal pyramid representation to capture the regional nonlinear motion and (2) a holistic event-frame feature extractor for long-term dependence and continuity motion. We then propose a novel INR-based decoder with spatiotemporal embeddings to capture long-term dependencies with a larger temporal perception field. We validate the effectiveness and generalization of our method on four datasets (both simulated and real data), showing the superiority of our method.
Supplementary Material: zip
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 1238
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