Summary of Motif: Making Text Count in Image Animation with Motion Focal Loss, by Shijie Wang et al.
MotiF: Making Text Count in Image Animation with Motion Focal Loss
by Shijie Wang, Samaneh Azadi, Rohit Girdhar, Saketh Rambhatla, Chen Sun, Xi Yin
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to generating videos from images with text descriptions, referred to as text-guided image animation. The existing methods struggle to align the generated video with the text prompts, particularly when motion is specified. To overcome this limitation, the authors introduce MotiF, a simple yet effective method that directs the model’s learning to regions with more motion. This approach improves the text alignment and motion generation by using optical flow to generate a motion heatmap and weighting the loss according to the intensity of the motion. The paper also proposes TI2V Bench, a dataset consisting of 320 image-text pairs for robust evaluation. Through comprehensive evaluation on TI2V Bench, MotiF outperforms nine open-sourced models, achieving an average preference of 72%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about generating videos from images with text descriptions. This is called text-guided image animation. Some methods don’t do a great job at matching the video to what the text says, especially when there’s motion involved. To fix this, the authors created MotiF, which helps the model learn better by focusing on areas with more movement. They also made a new dataset for testing called TI2V Bench. This dataset has 320 image-text pairs. The paper shows that MotiF does a better job than other methods at making videos that match what the text says. |
Keywords
» Artificial intelligence » Alignment » Optical flow
