Interpolation Benchmark

Video frame interpolation (VFI) benchmarks aim to objectively evaluate algorithms that synthesize new frames between existing ones, improving video smoothness. Current research focuses on developing large-scale datasets with diverse video characteristics and consistent evaluation metrics to enable fair comparisons between methods, including those employing transformer networks, wavelet transforms, and enhanced motion estimation techniques. These benchmarks are crucial for advancing VFI algorithms, ultimately leading to higher-quality video experiences in various applications such as video editing, upscaling, and slow-motion effects. Improved efficiency and accuracy in handling complex motions, such as those involving large displacements or occlusions, remain key challenges.