

Video Depth Anything
Overview :
Video Depth Anything is a deep learning-based video depth estimation model capable of providing high-quality and temporally consistent depth estimates for super-long videos. This technology is developed based on Depth Anything V2, boasting strong generalization capabilities and stability. Its primary advantages include the ability to estimate depth for any length of video, temporal consistency, and adaptability to open-world video. The model was developed by ByteDance's research team to address challenges in depth estimation for long videos, such as temporal consistency and adaptability in complex scenes. The code and demos for this model are currently available for researchers and developers.
Target Users :
This product is suitable for computer vision researchers, deep learning developers, and businesses or institutions that need to perform in-depth video analysis. It provides critical technical support for video content understanding, augmented reality applications, and three-dimensional reconstruction.
Use Cases
Providing real-time depth estimation of the environment surrounding vehicles in autonomous driving scenarios to assist in decision-making.
Offering precise depth information for post-production visual effects in film production, facilitating the integration of virtual and real scenes.
Generating immersive three-dimensional video experiences for users in virtual reality applications, enhancing user interaction.
Features
Supports depth estimation for super-long videos without length limitations.
Provides high-quality depth map outputs suitable for various application scenarios.
Ensures continuity and consistency in depth estimation over time.
Exhibits good generalization capabilities for open-world videos, adapting well to complex scenes.
Offers code and online demos for convenient use by researchers and developers.
Integrates with the MoGe model for camera parameter calibration and depth map alignment.
How to Use
Visit the project homepage to learn about the model's basic information and features.
Download the code and pre-trained models, and install necessary dependencies.
Prepare the input video, ensuring the format complies with the model requirements.
Run the model to perform depth estimation on the video and generate depth maps.
Process or analyze the depth maps further as needed.
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