EGVD: Event-Guided Video Diffusion Model for Physically Realistic Large-Motion Frame Interpolation

• URL: http://arxiv.org/abs/2503.20268v1
• Date: Wed, 26 Mar 2025 06:33:32 GMT
• Title: EGVD: Event-Guided Video Diffusion Model for Physically Realistic Large-Motion Frame Interpolation
• Authors: Ziran Zhang, Xiaohui Li, Yihao Liu, Yujin Wang, Yueting Chen, Tianfan Xue, Shi Guo,
• Abstract summary: Event-Guided Video Diffusion Model (EGVD) is a novel framework that leverages the powerful priors of pre-trained stable video diffusion models.<n>Our approach features a Multi-modal Motion Condition Generator (MMCG) that effectively integrates RGB frames and event signals to guide the diffusion process.<n>Experiments on both real and simulated datasets demonstrate that EGVD significantly outperforms existing methods in handling large motion.
• Score: 16.22243283808375
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Video frame interpolation (VFI) in scenarios with large motion remains challenging due to motion ambiguity between frames. While event cameras can capture high temporal resolution motion information, existing event-based VFI methods struggle with limited training data and complex motion patterns. In this paper, we introduce Event-Guided Video Diffusion Model (EGVD), a novel framework that leverages the powerful priors of pre-trained stable video diffusion models alongside the precise temporal information from event cameras. Our approach features a Multi-modal Motion Condition Generator (MMCG) that effectively integrates RGB frames and event signals to guide the diffusion process, producing physically realistic intermediate frames. We employ a selective fine-tuning strategy that preserves spatial modeling capabilities while efficiently incorporating event-guided temporal information. We incorporate input-output normalization techniques inspired by recent advances in diffusion modeling to enhance training stability across varying noise levels. To improve generalization, we construct a comprehensive dataset combining both real and simulated event data across diverse scenarios. Extensive experiments on both real and simulated datasets demonstrate that EGVD significantly outperforms existing methods in handling large motion and challenging lighting conditions, achieving substantial improvements in perceptual quality metrics (27.4% better LPIPS on Prophesee and 24.1% on BSRGB) while maintaining competitive fidelity measures. Code and datasets available at: https://github.com/OpenImagingLab/EGVD.

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