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Video2GIF: Automatic Generation of Animated GIFs from Video

Michael Gygli, Yale Song, Liangliang Cao

arXiv:1605.04850v1cs.CVcs.MM

TL;DR

Video2GIF addresses the manual problem of selecting suitable short, silent looping segments from videos. It trains a Robust Deep RankNet on user-generated GIFs and corresponding source videos, using adaptive Huber ranking to handle noisy data. The approach outperforms existing methods on the GIF dataset and generalizes to highlight detection.

  • Problem

    Video2GIF asks how to automate the manual and elaborate process of selecting video segments for animated GIF creation.

  • Method

    A Robust Deep RankNet ranks video segments by GIF suitability using visual and contextual representations, pairwise comparisons, and an adaptive Huber ranking loss trained from large-scale GIF data.

  • Results

    The method outperforms existing methods on the animated-GIF dataset and generalizes well to highlight detection.

  • Takeaways & Limitations

    Large-scale user-generated GIFs provide supervision for learning subtle segment preferences while the adaptive rank loss handles noisy web data and encodes content quality.

Abstract

from arXiv · show

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.

1. Introduction

The paper frames Video2GIF as a new task for automating the laborious selection of short, silent video segments suitable for animated GIFs. It proposes a robust ranking framework trained on large-scale user-generated GIF data to learn these preferences.

  • 1. Introduction: Video2GIF introduces automatic generation of animated GIFs from video, addressing the manual selection of clip start and end times.GIFs are short, silent, looping visual media with applications including photojournalism, advertising, video sharing, and social-media promotion.
  • 1. Introduction: The proposed RankNet ranks a video's segments by GIF suitability rather than treating the task as ordinary classification.The framework compares segment pairs and is designed to learn subtle differences in content selected for GIFs.
  • 1. Introduction: The method uses 3D convolutional features, pairwise segment ranking, and an adaptive Huber loss to model dynamic visual content and handle noisy web data.The loss is intended to improve robustness to outliers while representing differing degrees of content quality.
  • 1. Introduction: The authors collect over 100K user-generated GIFs with corresponding video sources and train on more than 500K GIF/non-GIF segment pairs.This dataset supplies weak supervision from content that users selected for GIF creation.
  • 1. Introduction: The paper contributes a new task, a Robust Deep RankNet with adaptive Huber ranking loss, and a publicly available large-scale benchmark dataset.The dataset is described as one to two orders of magnitude larger than existing video highlighting and summarization datasets.

2. Related Work

Prior work studies visual quality, interestingness, popularity, highlights, summarization, ranking, and learning from weak or noisy labels. Video2GIF is positioned within these areas while using large-scale GIF-derived supervision for segment ranking.

  • Image aesthetics and interestingness: Image research has modeled aesthetics, visual interestingness, popularity, and creativity, often relying on subjective or noisy judgments.The cited work includes ranking-based approaches that account for outliers and analyses of popularity in large image collections.
  • Video highlight detection and summarization: Video highlight methods use annotated topic-specific data, crawled edited videos, or unsupervised assumptions about how highlights appear in short videos.The reviewed approaches differ in supervision, dataset scale, and whether models are domain-specific or unsupervised.
  • Learning to rank with deep neural networks: Deep ranking research commonly formulates learning over pairs or triplets, with relative losses applied to CNN outputs or Siamese-style representations.These methods provide precedents for learning ordering constraints rather than only predicting independent labels.
  • Supervised deep learning from noisy labels: Prior studies also demonstrate deep learning from weak or noisy labels generated through clicks, automatic procedures, or social-media data.These works motivate using large-scale but imperfect supervision for visual representation learning.
  • Dataset context: The cited related-work passages include dataset statistics and video-length visualizations, but do not establish a specific related-work comparison from those visuals.The supplied Figure 2 caption identifies input-video length distribution without reporting its values.

3. Video2GIF Dataset

The dataset combines large-scale GIF–video pairs aligned by frame matching, with analysis of video content and controlled splits for evaluation.

  • Dataset Collection: 120K animated GIFs and 80K videos spanning 7,379 hours form a dataset one to two orders of magnitude larger than prior highlight datasets.The data were harvested from GIF-creation websites and linked to corresponding source videos.
  • Alignment: Frame matching with perceptual hashes aligns GIFs to source videos efficiently, requiring O(nk) comparisons while typically keeping k < 50.Binary perceptual hashes are compared using Hamming distance, enabling accurate alignment at low computational cost.
  • Alignment Evaluation: Manual evaluation on 20 GIFs reports a mean alignment error of 0.34 seconds and median error of 0.20 seconds, versus approximately two-second blocks in prior work.The measured accuracy was considered sufficient for the task.
  • Dataset Analysis: The dataset contains mostly short source videos, with a median duration of 2m51s and a mean duration of 5m12s.Video tags vary in informativeness, while categories are highly imbalanced and often broad.
  • Splits: Videos longer than 10 minutes were discarded, and the data were split into approximately 65K training, 5K validation, and 357 Creative Commons test videos.The test set uses Creative Commons videos to support future distribution and research.

4. Method

The method segments videos, represents their spatio-temporal content, and ranks segments by GIF suitability using a pairwise Robust Deep RankNet. An adaptive Huber ranking loss addresses noisy and uneven-quality web data while the model produces a ranked list at test time.

  • Segment preprocessing: Videos are divided into non-overlapping segments, with GIF positives identified by more than 66% overlap and non-overlapping segments treated as negatives.Shot boundary detection supplies the initial segmentation.
  • Architecture overview: The model scores every video segment and returns a ranked list of segments by GIF suitability at test time.It learns a scoring function by comparing GIF and non-GIF segments during training.
  • Feature representation: C3D features represent each segment's spatial and temporal dynamics, optionally supplemented with category, tag, timestamp, rank, and relative-position context.The contextual features can condition rankings on video semantics and position.
  • Problem formulation: Because GIF suitability varies by video context, the ranking compares positive segments only with negative segments from the same video rather than across videos.The formulation uses pairwise constraints because no clear binary boundary separates good and bad GIF segments.
  • Robust ranking loss: The adaptive Huber loss is squared for small margin violations and linear for stronger violations, combining l2-like behavior near the margin with l1-like robustness to outliers.The loss addresses low-quality user-generated GIFs; its transition point δ is GIF-dependent and higher for more popular GIFs.
  • Architecture and training: The network uses two fully connected hidden layers with ReLU activations and a linear output, trained with mini-batch stochastic gradient descent, dropout, and model averaging.The implementation uses 512 and 128 hidden units, 500K training pairs, and adaptive δ = 1.5 + p, where p is normalized viewcount.

5. Experiments

Experiments compare Video2GIF with ranking, semantic, and category-specific baselines on GIF suitability and cross-dataset highlight detection. The method outperforms the baselines and generalizes competitively across tasks.

  • 5.2. Results and Discussions: Our method outperforms baseline methods by a large margin in nMSD, while rankSVM is the strongest baseline.The comparison uses Table 2, where lower nMSD and higher mAP indicate better performance.
  • 5.2. Results and Discussions: The GIF-suitability ranking objective helps both our method and rankSVM outperform semantic-similarity baselines trained against video categories or titles.The ranking baselines use positive and negative segments from the same video, unlike the proxy objectives of the other methods.
  • 5.2. Results and Discussions: The adaptive Huber rank loss performs better than l1 and l2 losses by reducing sensitivity to outliers while avoiding excessive penalties for small margin violations.The loss combines l1-like robustness to outliers with the reduced gradient behavior associated with l2 loss.
  • 5.2. Results and Discussions: The qualitative results show that the model favors people in motion and soccer goal areas, while often rejecting low-contrast segments without main objects.A reported failure case shows that these learned preferences are not universally reliable.
  • 5.3. Cross Dataset Performance: On cross-dataset highlight detection, our single global model trained on GIF data outperforms rankSVM and Yang et al., although Sun et al. performs best when trained directly per category.The GIF-trained model uses more diverse video categories and remains competitive despite not using category-specific training.

6. Conclusion

The paper introduces Video2GIF and a Robust Deep RankNet with an adaptive Huber loss for ranking video segments by GIF suitability. It reports improved ranking over existing methods and generalization to highlight detection, while identifying multi-shot GIF selection as future work.

  • 6. Conclusion: The Robust Deep RankNet ranks video segments by GIF suitability and outperforms existing methods on the new animated-GIF dataset.The model learns rankings despite subtle differences between candidate segments.
  • 6. Conclusion: The adaptive Huber rank loss handles noisy web data, remains robust to outliers, and encodes content quality in the ranking objective.The loss is presented as the mechanism supporting learning from user-generated GIF data.
  • 6. Conclusion: The model generalizes well to highlight detection beyond its original GIF-suitability task.This conclusion is supported by the reported cross-task evaluation.
  • 6. Conclusion: The study considers only single segments, leaving selection and joint segmentation of multi-shot GIFs for future research.The paper also suggests using richer language models for video metadata and tag embeddings.
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