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Mads Kuhlmann-Joergensen
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The Sora Video Generation Aligned Words dataset contains a collection of word segments for text-to-video or other multimodal research. It is intended to help researchers and engineers explore fine-grained prompts, including those where certain words are not aligned with the video.
We hope this dataset will support your work in prompt understanding and advance progress in multimodal projects.
If you have specific questions, feel free to reach out.
https://huggingface.co/datasets/Rapidata/sora-video-generation-aligned-words
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This dataset was collected in roughly 4 hours using the Rapidata Python API, showcasing how quickly large-scale annotations can be performed with the right tooling!
All that at less than the cost of a single hour of a typical ML engineer in Zurich!
The new dataset of ~22,000 human annotations evaluating AI-generated videos based on different dimensions, such as Prompt-Video Alignment, Word for Word Prompt Alignment, Style, Speed of Time flow and Quality of Physics.
https://huggingface.co/datasets/Rapidata/text-2-video-Rich-Human-Feedback
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Runway Gen-3 Alpha: The Style and Coherence Champion
Runway's latest video generation model, Gen-3 Alpha, is something special. It ranks #3 overall on our text-to-video human preference benchmark, but in terms of style and coherence, it outperforms even OpenAI Sora.
However, it struggles with alignment, making it less predictable for controlled outputs.
We've released a new dataset with human evaluations of Runway Gen-3 Alpha: Rapidata's text-2-video human preferences dataset. If you're working on video generation and want to see how your model compares to the biggest players, we can benchmark it for you.
🚀 DM us if you’re interested!
Dataset: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-runway-alpha
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