Neural Video Depth Stabilizer by Adobe, An Overview…
The Neural Video Depth Stabilizer (NVDS) is a groundbreaking framework that aims to address the challenges in video depth estimation. Developed by a team of researchers from the School of Artificial Intelligence and Automation at Huazhong University of Science and Technology, Adobe Research, and S-Lab at Nanyang Technological University, NVDS offers a plug-and-play solution that can be applied to various single-image depth models to stabilize inconsistent depth estimations.
The NVDS Framework
The NVDS framework comprises a depth predictor and a stabilization network. The depth predictor can be any single-image depth model, which provides initial flickering disparity estimations. The stabilization network then refines these estimations into temporally consistent results. This plug-and-play refiner can be used with different depth predictors, making it a versatile tool for depth stabilization.
Cross-Attention Module and Bidirectional Inference
The stabilization network uses a cross-attention module to refine depth-aware features with temporal information from relevant frames. This allows each frame to attend to relevant information from adjacent frames for temporal consistency. The researchers also designed a bidirectional inference strategy to further improve consistency.
Video Depth in the Wild (VDW) Dataset
To support the training of robust learning-based models, the researchers introduced a large-scale dataset called Video Depth in the Wild (VDW). This dataset consists of over two million frames from 14,203 videos, making it the largest natural-scene video depth dataset to date. The VDW dataset includes videos from diverse sources, including movies, animations, documentaries, and web videos.
Performance and Efficiency
The NVDS framework was evaluated on the VDW dataset and two public benchmarks. It demonstrated significant improvements in consistency, accuracy, and efficiency compared to previous approaches. The framework’s plug-and-play manner proved to be flexible and effective, demonstrating its potential to serve as a solid baseline for learning-based video depth models.
Conclusion
The NVDS framework represents a significant advancement in video depth estimation. By offering a plug-and-play solution that can be applied to various single-image depth models, it provides a versatile tool for depth stabilization. Coupled with the large-scale VDW dataset, the NVDS framework sets a new standard for video depth estimation research.
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