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- This is the official repository of TI2V-Bench dataset proposed in the paper [MotiF: Making Text Count in Image Animation with Motion Focal Loss](https://wang-sj16.github.io/motif/).
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  TI2V-Bench comprises text-image pairs from 22 diverse scenarios featuring a variety of objects and scenes. Each scenario includes 3 to 5 images with similar content presented in different styles, alongside multiple distinct prompts designed to animate these images and generate varied outputs. The benchmark includes a total of 320 image-text pairs, consisting of 88 unique images and 133 unique prompts.
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  Check ``prompt.csv`` for the image path and corresponding prompts.
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- You can also download the dataset from Google Drive through [this link](https://drive.google.com/file/d/1y6kU3xSEw67thDQWsmwW3aqsvpCCrOzw/view?usp=sharing).
 
 
 
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+ This is the official repository of TI2V-Bench dataset proposed in the paper [MotiF: Making Text Count in Image Animation with Motion Focal Loss](arxiv.org/abs/2412.16153).
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  TI2V-Bench comprises text-image pairs from 22 diverse scenarios featuring a variety of objects and scenes. Each scenario includes 3 to 5 images with similar content presented in different styles, alongside multiple distinct prompts designed to animate these images and generate varied outputs. The benchmark includes a total of 320 image-text pairs, consisting of 88 unique images and 133 unique prompts.
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  Check ``prompt.csv`` for the image path and corresponding prompts.
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+ You can also download the dataset from Google Drive through [this link](https://drive.google.com/file/d/1y6kU3xSEw67thDQWsmwW3aqsvpCCrOzw/view?usp=sharing).
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+ Check more details about this work in our [project page](https://wang-sj16.github.io/motif/).