Summary of Sg-i2v: Self-guided Trajectory Control in Image-to-video Generation, by Koichi Namekata et al.
SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation
by Koichi Namekata, Sherwin Bahmani, Ziyi Wu, Yash Kant, Igor Gilitschenski, David B. Lindell
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 SG-I2V, a framework for controllable image-to-video generation that relies solely on a pre-trained image-to-video diffusion model without the need for fine-tuning or external knowledge. The method addresses the issue of adjusting specific elements in generated videos, such as object motion or camera movement, which is often a tedious process of trial and error. SG-I2V outperforms unsupervised baselines while narrowing down the performance gap with supervised models in terms of visual quality and motion fidelity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make videos from pictures that can be controlled easily. This is useful because it’s hard to adjust things like object movement or camera angles when making videos. The method uses a special kind of model that doesn’t need to be fine-tuned or given extra information. It works well and makes the video quality and motion smoother. |
Keywords
» Artificial intelligence » Diffusion model » Fine tuning » Supervised » Unsupervised
