# AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation

## TL;DR

AdaCoF introduces an adaptive warping module for video frame interpolation that effectively handles complex motions, outperforming existing methods on benchmark datasets.

## Contribution

The paper presents AdaCoF, a generalized warping module estimating kernel weights and offsets, and introduces a dual-frame adversarial loss for improved realistic frame synthesis.

## Key findings

- Outperforms state-of-the-art methods on benchmarks.
- Handles complex motions more effectively.
- Produces more realistic interpolated frames.

## Abstract

Video frame interpolation is one of the most challenging tasks in video processing research. Recently, many studies based on deep learning have been suggested. Most of these methods focus on finding locations with useful information to estimate each output pixel using their own frame warping operations. However, many of them have Degrees of Freedom (DoF) limitations and fail to deal with the complex motions found in real world videos. To solve this problem, we propose a new warping module named Adaptive Collaboration of Flows (AdaCoF). Our method estimates both kernel weights and offset vectors for each target pixel to synthesize the output frame. AdaCoF is one of the most generalized warping modules compared to other approaches, and covers most of them as special cases of it. Therefore, it can deal with a significantly wide domain of complex motions. To further improve our framework and synthesize more realistic outputs, we introduce dual-frame adversarial loss which is applicable only to video frame interpolation tasks. The experimental results show that our method outperforms the state-of-the-art methods for both fixed training set environments and the Middlebury benchmark.