Video Frame Interpolation
95 papers with code • 20 benchmarks • 11 datasets
The goal of Video Frame Interpolation is to synthesize several frames in the middle of two adjacent frames of the original video. Video Frame Interpolation can be applied to generate slow motion video, increase video frame rate, and frame recovery in video streaming.
Benchmarks
These leaderboards are used to track progress in Video Frame Interpolation
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Libraries
Use these libraries to find Video Frame Interpolation models and implementationsDatasets
UCF101
DAVIS
GoPro
Middlebury
Vimeo90K
X4K1000FPS
MSU Video Frame Interpolation
Inter4K
SlowFlow
ATD-12K
Subtasks
Latest papers with no code
Motion-aware Latent Diffusion Models for Video Frame Interpolation
With the advancement of AIGC, video frame interpolation (VFI) has become a crucial component in existing video generation frameworks, attracting widespread research interest.
Event-Enhanced Snapshot Compressive Videography at 10K FPS
To unlock the potential of conventional snapshot compressive videography, we propose a novel hybrid "intensity+event" imaging scheme by incorporating an event camera into a video SCI setup.
Sparse Global Matching for Video Frame Interpolation with Large Motion
Large motion poses a critical challenge in Video Frame Interpolation (VFI) task.
Perception-Oriented Video Frame Interpolation via Asymmetric Blending
In practice, motion estimates often prove to be error-prone, resulting in misaligned features.
Benchmarking Video Frame Interpolation
Video frame interpolation, the task of synthesizing new frames in between two or more given ones, is becoming an increasingly popular research target.
Video Frame Interpolation with Region-Distinguishable Priors from SAM
In existing Video Frame Interpolation (VFI) approaches, the motion estimation between neighboring frames plays a crucial role.
Perceptual Quality Assessment for Video Frame Interpolation
To evaluate the quality of VFI frames without reference videos, a no-reference perceptual quality assessment method is proposed in this paper.
Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement
In this work, we first propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently.
Three-Stage Cascade Framework for Blurry Video Frame Interpolation
Besides, experiments on real-world blurry videos also indicate the good generalization ability of our model.
SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame Interpolation
We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets.
