TIP-I2V - A large-scale dataset of over 1.7 million real-world text and image cues.
TIP-I2V is a large-scale dataset of real-world text and image cues used in image-to-video generation. TIP-I2V contains over 1.7 million unique user-generated text and image cues, along with corresponding videos generated by five state-of-the-art (SOTA) image-to-video models...
What is TIP-I2V?
TIP-I2V is a large-scale dataset of real-world text and image cues used in the field of image-to-video generation. TIP-I2V contains over 1.7 million unique user-generated text and image cues, along with corresponding videos generated by five state-of-the-art (SOTA) image-to-video models. This dataset can drive the development of better and safer image-to-video models, helping researchers analyze user preferences, evaluate model performance, and address misinformation issues arising from image-to-video models.
Main functions of TIP-I2V
- User Preference AnalysisBy analyzing user-submitted text and image prompts, researchers can understand users' needs and preferences for image-to-video generation.
- Model performance evaluationIt provides a platform that allows researchers to evaluate and compare the performance of different image-to-video generation models using real user data.
- Security and error message researchIt helps researchers solve the problem of misinformation caused by image-to-video models, such as video generation techniques creating false content.
TIP-I2V Technical Principles
- Data collectionWe collected over 1.7 million text and image cues from sources such as the Pika Discord channel, and generated corresponding video results.
- Multi-model integrationIt integrates five different image-to-video diffusion models (Pika, Stable Video Diffusion, Open-Sora, I2VGen-XL, CogVideoX-5B) to generate videos, providing diverse data.
- Metadata annotationAssign metadata such as UUID, timestamp, topic, NSFW (Not Suitable for the Workplace) status, text and image embeddings to each data point.
- Semantic analysisBased on natural language processing techniques, such as GPT-4o, it analyzes verbs in text prompts and uses the HDBSCAN clustering algorithm to identify and rank the most popular topics.
- Video generation technology: Applying diffusion modeling technology, a generative model, to generate coherent video content from static images.
- Security and Authentication: Develop and evaluate models for identifying generated videos and tracing video source images to prevent videos from being misused for the spread of misinformation.
TIP-I2V project address
- Project official website:tip-i2v.github.io
- GitHub repository:https://github.com/WangWenhao0716/TIP-I2V
- HuggingFace model library:https://huggingface.co/datasets/WenhaoWang/TIP-I2V
- arXiv technical paper:https://arxiv.org/pdf/2411.04709
Application scenarios of TIP-I2V
- Content creation and entertainmentIndependent artists can easily transform static paintings into dynamic videos for use in exhibitions or online galleries.
- Advertising and MarketingThe marketing team created engaging video ads using product images to increase click-through rates for online ads.
- Education and TrainingEducational institutions transform complex scientific concept images into easy-to-understand animated videos to aid teaching.
- News and ReportsNews organizations convert photos from news scenes into videos, providing viewers with more intuitive news reports.
- Art and DesignDigital artists transform static artworks into dynamic displays, creating new art experiences.