Adapting Image-to-Video Diffusion Models for Large-Motion Frame Interpolation
This addresses video frame interpolation for large-motion scenarios, representing an incremental improvement in generative-based methodologies.
The paper tackled video frame interpolation for large-motion scenarios by adapting image-to-video diffusion models with a conditional encoder, dual-branch feature extractor, and cross-frame attention mechanism, achieving superior performance on the Fréchet Video Distance metric compared to state-of-the-art approaches.
With the development of video generation models has advanced significantly in recent years, we adopt large-scale image-to-video diffusion models for video frame interpolation. We present a conditional encoder designed to adapt an image-to-video model for large-motion frame interpolation. To enhance performance, we integrate a dual-branch feature extractor and propose a cross-frame attention mechanism that effectively captures both spatial and temporal information, enabling accurate interpolations of intermediate frames. Our approach demonstrates superior performance on the Fréchet Video Distance (FVD) metric when evaluated against other state-of-the-art approaches, particularly in handling large motion scenarios, highlighting advancements in generative-based methodologies.