Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation

• URL: http://arxiv.org/abs/2412.07761v1
• Date: Tue, 10 Dec 2024 18:55:30 GMT
• Title: Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation
• Authors: Jingxi Chen, Brandon Y. Feng, Haoming Cai, Tianfu Wang, Levi Burner, Dehao Yuan, Cornelia Fermuller, Christopher A. Metzler, Yiannis Aloimonos,
• Abstract summary: Event-based Video Frame Interpolation (EVFI) uses sparse, high-temporal-resolution event measurements as motion guidance. We adapt pre-trained video diffusion models trained on internet-scale datasets to EVFI. Our method outperforms existing methods and generalizes across cameras far better than existing approaches.
• Score: 20.689304579898728
• License:
• Abstract: Video Frame Interpolation aims to recover realistic missing frames between observed frames, generating a high-frame-rate video from a low-frame-rate video. However, without additional guidance, the large motion between frames makes this problem ill-posed. Event-based Video Frame Interpolation (EVFI) addresses this challenge by using sparse, high-temporal-resolution event measurements as motion guidance. This guidance allows EVFI methods to significantly outperform frame-only methods. However, to date, EVFI methods have relied on a limited set of paired event-frame training data, severely limiting their performance and generalization capabilities. In this work, we overcome the limited data challenge by adapting pre-trained video diffusion models trained on internet-scale datasets to EVFI. We experimentally validate our approach on real-world EVFI datasets, including a new one that we introduce. Our method outperforms existing methods and generalizes across cameras far better than existing approaches.

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