# Video2GIF: Automatic Generation of Animated GIFs from Video

arXiv: https://arxiv.org/abs/1605.04850 · HTML: https://mlsanity.com/paper/1605.04850/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2016.

## Built on by (most cited first, 30 of 41)

- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2203.12745/) (2022)
- [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://mlsanity.com/paper/2107.09609/) (2021)
- [Cross-category Video Highlight Detection via Set-based Learning](https://mlsanity.com/paper/2108.11770/) (2021)
- [R2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding](https://mlsanity.com/paper/2404.00801/) (2024)
- [Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding](https://mlsanity.com/paper/2311.08835/) (2023)
- [Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval](https://mlsanity.com/paper/2407.15051/) (2024)
- [FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding](https://mlsanity.com/paper/2412.13441/) (2024)
- [Saliency-Guided DETR for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2410.01615/) (2024)
- [Multimodal Action Quality Assessment](https://mlsanity.com/paper/2402.09444/) (2024)
- [Learning Pixel-Level Distinctions for Video Highlight Detection](https://mlsanity.com/paper/2204.04615/) (2022)
- [MS-DETR: Towards Effective Video Moment Retrieval and Highlight Detection by Joint Motion-Semantic Learning](https://mlsanity.com/paper/2507.12062/) (2025)
- [Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning](https://mlsanity.com/paper/2206.10157/) (2022)
- [AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant](https://mlsanity.com/paper/2111.15050/) (2021)
- [LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2501.10787/) (2025)
- [Unsupervised Modality-Transferable Video Highlight Detection with Representation Activation Sequence Learning](https://mlsanity.com/paper/2403.09401/) (2024)
- [OmniSTVG: Toward Spatio-Temporal Omni-Object Video Grounding](https://mlsanity.com/paper/2503.10500/) (2025)
- [PR-Net: Preference Reasoning for Personalized Video Highlight Detection](https://mlsanity.com/paper/2109.01799/) (2021)
- [Video Summarization Overview](https://mlsanity.com/paper/2210.11707/) (2022)
- [Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence](https://mlsanity.com/paper/2407.13933/) (2024)
- [Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies](https://mlsanity.com/paper/2303.14768/) (2023)
- [HighlightMe: Detecting Highlights from Human-Centric Videos](https://mlsanity.com/paper/2110.01774/) (2021)
- [Unsupervised Video Summarization via Iterative Training and Simplified GAN](https://mlsanity.com/paper/2311.03745/) (2023)
- [Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention](https://mlsanity.com/paper/2207.08352/) (2022)
- [DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2308.15109/) (2023)
- [ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding](https://mlsanity.com/paper/2408.06622/) (2024)
- [Test-Time Adaptation for Video Highlight Detection Using Meta-Auxiliary Learning and Cross-Modality Hallucinations](https://mlsanity.com/paper/2508.04924/) (2025)
- [Exploring Global Diversity and Local Context for Video Summarization](https://mlsanity.com/paper/2201.11345/) (2022)
- [Videogenic: Identifying Highlight Moments in Videos with Professional Photographs as a Prior](https://mlsanity.com/paper/2211.12493/) (2022)

## Abstract (arXiv)

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.