RollingDepth - Estimate Depth in Videos without Video Models - Install and Test Locally
AI Summary
The video introduces “Rolling Depth,” a new technique for estimating depth in video sequences that improves over single image depth estimators and video depth models. The presenter demonstrates installing the Rolling Depth system on a local Ubuntu machine with an Nvidia RTX A6000 GPU, explaining the setup and dependencies.
Rolling Depth transforms a single image latent diffusion model into a video depth estimator by processing short video snippets using modified cross-frame self attention and assembling them into temporally consistent depth videos via a robust registration algorithm. The method operates with rolling inference, sampling video snippets at different temporal resolutions to maintain memory efficiency while capturing local and long-range temporal consistency.
The video also covers the architecture briefly and shows how the model excels in producing stable, flicker-free depth videos for various clips including horses, humans, and moving objects. The demonstration highlights low VRAM usage relative to other models like Merry Gold, and the model’s ability to handle complex scenes effectively.
Applications in autonomous driving, robotics, augmented reality, and content creation for reliable 3D scene understanding are mentioned. The video ends with a call to like, share, and subscribe, and links to the GitHub repo and sponsor.
Overall, the video is a practical walkthrough of installing and testing state-of-the-art Video Depth Estimation with Rolling Depth, supported by clear examples and technical insights.