DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos Without Supplementary Data

Estimating depth in open-world videos is a significant challenge due to the wide range of appearances, content motion, camera movements, and varying video lengths. A new paper from researchers at various institutions introduces DepthCrafter, an innovative method that generates temporally consistent long depth sequences with intricate details for such videos, without needing additional information like camera poses or optical flow.

Technical Overview

Key Contributions:

• Consistent Depth Sequences: DepthCrafter can generate depth sequences up to 110 frames at a time, maintaining temporal consistency and detailed accuracy.
• Generalization to Open-world Videos: The model is trained using a pre-trained image-to-video diffusion model and a three-stage training strategy, enabling it to handle diverse video content effectively.
• Zero-shot Performance: DepthCrafter achieves state-of-the-art performance in open-world video depth estimation without any fine-tuning or additional data.
• Downstream Applications: The generated depth sequences can be used for various applications, such as depth-based visual effects and conditional video generation.

Training Strategy

The training process is divided into three stages:

• Stage 1: Pre-training on Image-to-Video Diffusion Model

  • The model starts with a pre-trained image-to-video diffusion model. This initial step ensures that the network has a strong foundation in understanding both static and dynamic visual elements.

• Stage 2: Fine-tuning for Depth Estimation

  • In this stage, the model is fine-tuned specifically for depth estimation tasks. The training data includes realistic and synthetic datasets to ensure robustness across different scenarios.

• Stage 3: Temporal Consistency Refinement

  • The final stage focuses on refining temporal consistency. This ensures that the generated depth sequences are smooth and coherent over time, which is crucial for applications like video editing and augmented reality.

Inference Strategy

For processing extremely long videos, DepthCrafter employs a segment-wise estimation approach followed by seamless stitching:

• Segment-Wise Estimation: The video is divided into manageable segments. Each segment is processed independently to generate depth sequences.
• Seamless Stitching: After generating the depth sequences for each segment, they are stitched together using techniques that ensure smooth transitions and temporal consistency.

Benchmarks and Results

Comprehensive evaluations on multiple datasets demonstrate DepthCrafter's superior performance:

• Precision in Depth Details: The model consistently generates accurate depth details, even in complex scenes with rapid motion or significant camera movement.
• Rich Content Diversity: DepthCrafter can handle a wide range of video content, from realistic footage to synthetic environments.
• State-of-the-Art Performance: Under zero-shot settings, DepthCrafter outperforms existing methods in open-world video depth estimation.

Applications

The generated depth sequences have numerous applications:

• Depth-Based Visual Effects: The detailed and consistent depth data can be used to create realistic visual effects, such as depth-based lighting and shadows.
• Conditional Video Generation: Depth sequences can serve as conditional inputs for generating new videos with specific characteristics, enhancing the creative possibilities in video production.

Conclusion

DepthCrafter represents a significant advancement in open-world video depth estimation. By leveraging a pre-trained image-to-video diffusion model and a carefully designed training strategy, it achieves state-of-the-art performance without requiring additional data. The method's ability to handle long videos and its wide range of applications make it a valuable tool for researchers and practitioners in computer vision and related fields.