Summary of Image Conductor: Precision Control For Interactive Video Synthesis, by Yaowei Li et al.
Image Conductor: Precision Control for Interactive Video Synthesis
by Yaowei Li, Xintao Wang, Zhaoyang Zhang, Zhouxia Wang, Ziyang Yuan, Liangbin Xie, Yuexian Zou, Ying Shan
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed Image Conductor method enables precise control over camera transitions and object movements for generating video assets from a single image. The approach involves a well-cultivated training strategy, separating distinct camera and object motion by LoRA weights, as well as a camera-free guidance technique during inference to eliminate camera transitions. Additionally, a trajectory-oriented video motion data curation pipeline is developed for training. Quantitative and qualitative experiments demonstrate the method’s precision and fine-grained control in generating motion-controllable videos from images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image Conductor is a new way to create videos from still images. It lets you control how objects move and cameras transition between shots with great precision. This is important because creating these kinds of effects in real-world filmmaking can be very time-consuming and labor-intensive. The method uses special weights and guidance techniques to separate camera movements from object movements, making it possible to create realistic video animations. |
Keywords
» Artificial intelligence » Inference » Lora » Precision
