Summary of Dive: Taming Dino For Subject-driven Video Editing, by Yi Huang et al.
DIVE: Taming DINO for Subject-Driven Video Editing
by Yi Huang, Wei Xiong, He Zhang, Chaoqi Chen, Jianzhuang Liu, Mingfu Yan, Shifeng Chen
First submitted to arxiv on: 4 Dec 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 This paper proposes DINO-guided Video Editing (DIVE), a framework for subject-driven video editing based on diffusion models. Building on the success of DINO in image generation and editing, DIVE aims to maintain temporal consistency and motion alignment by leveraging semantic features extracted from a pretrained DINOv2 model. The framework employs these features to align with the motion trajectory of the source video, achieving high-quality editing results with robust motion consistency. Additionally, DIVE incorporates LoRAs (Low-Rank Adaptations) to register the target subject’s identity, enabling precise subject editing. This paper demonstrates the potential of DINO in video editing, showcasing diverse real-world experiments and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to edit videos by using a special type of model called DINO. The goal is to make it easy to edit videos by giving them a specific look or feel based on text prompts or images. To do this, the researchers developed a system called DIVE that uses the powerful features extracted from the DINO model to align with the motion in the video. This helps keep the edited parts looking smooth and natural. The paper also shows how DIVE can be used to edit specific people or objects within a video, making it more precise. Overall, this new approach has the potential to make video editing easier and more fun. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Image generation
