Towards Unified Keyframe Propagation Models
Patrick Esser, Peter Michael, Soumyadip Sengupta

TL;DR
This paper introduces a two-stream model combining local and global interactions to improve keyframe propagation in video editing, especially for high-frequency details, outperforming existing methods in inpainting tasks.
Contribution
The paper proposes a novel two-stream approach that separately handles high-frequency and low-frequency features for more accurate keyframe propagation in videos.
Findings
Improved propagation of high-frequency details in video inpainting.
Achieved 44% and 26% improvements in FID and LPIPS scores.
Robust performance in challenging scenarios like large camera motions.
Abstract
Many video editing tasks such as rotoscoping or object removal require the propagation of context across frames. While transformers and other attention-based approaches that aggregate features globally have demonstrated great success at propagating object masks from keyframes to the whole video, they struggle to propagate high-frequency details such as textures faithfully. We hypothesize that this is due to an inherent bias of global attention towards low-frequency features. To overcome this limitation, we present a two-stream approach, where high-frequency features interact locally and low-frequency features interact globally. The global interaction stream remains robust in difficult situations such as large camera motions, where explicit alignment fails. The local interaction stream propagates high-frequency details through deformable feature aggregation and, informed by the global…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Visual Attention and Saliency Detection · Video Analysis and Summarization
MethodsInpainting
