Summary of Streamingt2v: Consistent, Dynamic, and Extendable Long Video Generation From Text, by Roberto Henschel et al.
StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text
by Roberto Henschel, Levon Khachatryan, Daniil Hayrapetyan, Hayk Poghosyan, Vahram Tadevosyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi
First submitted to arxiv on: 21 Mar 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); Image and Video Processing (eess.IV)
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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 introduces StreamingT2V, an autoregressive text-to-video diffusion model that can generate long videos with smooth transitions. The model consists of three key components: a conditional attention module (CAM) for consistent chunk transitions, an appearance preservation module to prevent the model from forgetting initial scenes, and a randomized blending approach for infinitely long videos. The authors show that StreamingT2V outperforms competing methods in terms of motion amount and video quality, with high-quality seamless text-to-long video generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary StreamingT2V is a new way to make movies using text instructions! Right now, computers are good at making short videos, but they’re not very good at making long ones without it looking like the same thing over and over. This paper introduces a new way to do this called StreamingT2V. It uses special parts called CAM, appearance preservation module, and randomized blending to make sure the video flows smoothly and doesn’t get stuck in one place. The authors tested it and found that it makes videos with more motion and looks better than other ways of making long videos. |
Keywords
* Artificial intelligence * Attention * Autoregressive * Diffusion model
