Learning Temporally Consistent Video Depth from Video Diffusion Priors

Introduction

Monocular video depth estimation is a significant challenge in computer vision, crucial for applications like robotics, autonomous driving, and virtual reality. Achieving temporal consistency in depth estimation is particularly challenging due to the flickering artifacts that arise from single-frame scale ambiguities. The novel approach of leveraging video diffusion priors, as outlined in the ChronoDepth framework, presents a promising solution by reformulating the depth prediction task into a conditional generation problem.

ChronoDepth utilizes the Stable Video Diffusion (SVD) model to predict reliable depth from videos by first optimizing spatial layers and then temporal layers, ensuring both spatial accuracy and temporal consistency. This article provides a detailed explanation of the methodology, complete with code snippets and best practices for implementation.

Diffusion Formulation

To align with the video foundation model, we reformulate monocular video depth estimation as a conditional denoising diffusion generation task. The diffusion model…