Summary of Pix2gif: Motion-guided Diffusion For Gif Generation, by Hitesh Kandala et al.
Pix2Gif: Motion-Guided Diffusion for GIF Generation
by Hitesh Kandala, Jianfeng Gao, Jianwei Yang
First submitted to arxiv on: 7 Mar 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 Pix2Gif is a motion-guided diffusion model for generating images from text and video prompts. The paper tackles this task by formulating it as an image translation problem steered by text and motion magnitude prompts. To ensure the model adheres to motion guidance, the authors propose a new motion-guided warping module that spatially transforms features of the source image conditioned on the two types of prompts. A perceptual loss is also introduced to ensure the transformed feature map remains within the same space as the target image, ensuring content consistency and coherence. The model is trained using the TGIF video-caption dataset, which provides rich information about temporal changes. After pretraining, the model is applied in a zero-shot manner to various video datasets, demonstrating its effectiveness in capturing both semantic text prompts and spatial motion guidance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pix2Gif is a way for computers to make short videos from text and moving images. The goal is to create a realistic video that shows what the text describes and how it changes over time. To do this, the computer uses two kinds of information: words and movement. The words tell the computer what to show in the video, while the movement helps guide the computer’s creation of the video. The computer also makes sure that the video looks similar to the original images by using a special kind of math problem-solving. This way, the final video is both accurate and visually appealing. |
Keywords
» Artificial intelligence » Diffusion model » Feature map » Pretraining » Translation » Zero shot
