基於可泛化隱式運動建模的影片畫面插值技術
本文提出了一種新的可泛化隱式運動建模方法 (GIMM)，用於影片畫面插值，透過學習影片中複雜的運動模式，以產生任意時間點的高品質中間畫面。
Generalizable Implicit Motion Modeling for Video Frame Interpolation (GIMM)
This research paper introduces GIMM, a novel approach to video frame interpolation that leverages generalizable implicit neural representations for superior motion modeling, enabling the generation of high-quality intermediate frames at arbitrary timesteps.
Framer: Interactive and "Autopilot" Video Frame Interpolation Using Diffusion Models and Point Trajectory Control
Framer is a novel video frame interpolation framework that allows for both user-interactive and automated generation of smooth and visually appealing transitions between two images by leveraging the power of large-scale pre-trained video diffusion models and point trajectory control.
VFIMamba: Using Selective State Space Models for Efficient and High-Quality Video Frame Interpolation
VFIMamba leverages the strengths of Selective State Space Models (S6), particularly their efficiency and global receptive field, to achieve state-of-the-art performance in video frame interpolation, especially for high-resolution videos.
Motion-Aware Latent Diffusion Models for Realistic Video Frame Interpolation
The proposed Motion-Aware Latent Diffusion Model (MADIFF) effectively incorporates inter-frame motion priors between the target interpolated frame and the conditional neighboring frames to generate visually smooth and realistic interpolated video frames, significantly outperforming existing approaches.
An Efficient and High-Quality Video Frame Interpolation Framework with Large-Kernel Depth-wise Convolution and Decoder-only Refinement
An efficient video frame interpolation framework that achieves state-of-the-art performance with clear improvement while requiring much less computational resources.
Perception-Oriented Video Frame Interpolation with Asymmetric Blending
The core message of this paper is to introduce a novel perception-oriented video frame interpolation paradigm called PerVFI, which tackles the challenges of blur and ghosting artifacts by incorporating an asymmetric synergistic blending module and a conditional normalizing flow-based generator.
Video Interpolation with Diffusion Models: Generating High-Quality Intermediate Frames Between Input Frames
VIDIM, a generative model for video interpolation, creates short videos given a start and end frame by using cascaded diffusion models to generate the target video at low resolution and then at high resolution, enabling high-fidelity results even for complex, nonlinear, or ambiguous motions.
Comprehensive Benchmark for Evaluating Video Frame Interpolation Methods
This paper proposes a comprehensive benchmark for evaluating video frame interpolation methods. The benchmark includes a carefully designed synthetic test dataset that adheres to the constraint of linear motion, consistent error metrics, and an in-depth analysis of the interpolation quality with respect to various per-pixel attributes such as motion magnitude and occlusion.
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