Summary of Aid: Adapting Image2video Diffusion Models For Instruction-guided Video Prediction, by Zhen Xing and Qi Dai and Zejia Weng and Zuxuan Wu and Yu-gang Jiang
AID: Adapting Image2Video Diffusion Models for Instruction-guided Video Prediction
by Zhen Xing, Qi Dai, Zejia Weng, Zuxuan Wu, Yu-Gang Jiang
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 This paper introduces a novel approach to text-guided video prediction (TVP), which has applications in virtual reality, robotics, and content creation. Building upon previous methods that adapted Stable Diffusion for TVP, the authors tackle the limitations of these approaches by leveraging the strengths of both image-to-video diffusion models and textual control. They introduce the Multi-Modal Large Language Model (MLLM) to predict future video states based on initial frames and text instructions. The dual query transformer (DQFormer) architecture integrates instructions and frames into conditional embeddings for future frame prediction. Additionally, they develop Long-Short Term Temporal Adapters and Spatial Adapters to quickly adapt general video diffusion models to specific scenarios with minimal training costs. Experimental results show significant improvements over state-of-the-art techniques on four datasets: Something Something V2, Epic Kitchen-100, Bridge Data, and UCF-101. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about predicting what will happen in a video based on the first frame and some instructions. This can be useful for things like creating virtual reality experiences or controlling robots. The authors want to make this process better by combining two kinds of computer models: ones that are good at recognizing images and ones that understand text. They create a new model that can use both types of information to predict what will happen in the video. This model works well on four different datasets, which means it can be used for different tasks like creating kitchen videos or controlling robots. |
Keywords
» Artificial intelligence » Diffusion » Large language model » Multi modal » Transformer
