ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler
Serin Yang, Taesung Kwon, Jong Chul Ye

TL;DR
ViBiDSampler introduces a bidirectional diffusion sampling method that improves video keyframe interpolation by ensuring more coherent intermediate frames, achieving state-of-the-art quality and efficiency without extensive re-noising.
Contribution
The paper proposes a novel bidirectional sampling strategy for video interpolation that addresses off-manifold issues without re-noising, enhancing coherence and quality in generated videos.
Findings
Achieves state-of-the-art interpolation quality.
Interpolates 25 frames at 1024x576 in 195 seconds on a single GPU.
Effectively prevents artifacts common in previous methods.
Abstract
Recent progress in large-scale text-to-video (T2V) and image-to-video (I2V) diffusion models has greatly enhanced video generation, especially in terms of keyframe interpolation. However, current image-to-video diffusion models, while powerful in generating videos from a single conditioning frame, need adaptation for two-frame (start & end) conditioned generation, which is essential for effective bounded interpolation. Unfortunately, existing approaches that fuse temporally forward and backward paths in parallel often suffer from off-manifold issues, leading to artifacts or requiring multiple iterative re-noising steps. In this work, we introduce a novel, bidirectional sampling strategy to address these off-manifold issues without requiring extensive re-noising or fine-tuning. Our method employs sequential sampling along both forward and backward paths, conditioned on the start and end…
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Taxonomy
TopicsAdvanced Data Compression Techniques · Speech and Audio Processing · Image and Signal Denoising Methods
MethodsDiffusion
