Video2GIF: Automatic Generation of Animated GIFs from Video

TL;DR

This paper presents a deep learning approach to automatically generate animated GIFs from videos by ranking video segments based on their suitability, trained on a large dataset of user-generated GIFs.

Contribution

We introduce a Robust Deep RankNet that learns to identify the best video segments for GIF creation using noisy web data and a novel adaptive Huber loss.

Findings

Our method outperforms state-of-the-art approaches on a large-scale benchmark.

The model effectively captures patterns common in popular GIFs.

It demonstrates robustness to noisy and outlier data.

Abstract

We introduce the novel problem of automatically generating animated GIFs from video. GIFs are short looping video with no sound, and a perfect combination between image and video that really capture our attention. GIFs tell a story, express emotion, turn events into humorous moments, and are the new wave of photojournalism. We pose the question: Can we automate the entirely manual and elaborate process of GIF creation by leveraging the plethora of user generated GIF content? We propose a Robust Deep RankNet that, given a video, generates a ranked list of its segments according to their suitability as GIF. We train our model to learn what visual content is often selected for GIFs by using over 100K user generated GIFs and their corresponding video sources. We effectively deal with the noisy web data by proposing a novel adaptive Huber loss in the ranking formulation. We show that our approach is robust to outliers and picks up several patterns that are frequently present in popular animated GIFs. On our new large-scale benchmark dataset, we show the advantage of our approach over several state-of-the-art methods.