Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
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Cited by 33 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal inconsistency in videos, hindering its practical applications. Various methods have been proposed to alleviate this issue by leveraging video generation models or introducing priors from optical flow and camera poses. Nonetheless, these methods are only applicable to short videos (< 10 seconds) and require a trade-off between quality and computational efficiency. We propose Video Depth Anything for high-quality, consistent depth estimation in super-long videos (over several minutes) without sacrificing efficiency. We base our model on Depth Anything V2 and replace its head with an efficient spatial-temporal head. We design a straightforward yet effective temporal consistency loss by constraining the temporal depth gradient, eliminating the need for additional geometric priors. The model is trained on a joint dataset of video depth and unlabeled images, similar to Depth Anything V2. Moreover, a novel key-frame-based strategy is developed for long video inference. Experiments show that our model can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability. Comprehensive evaluations on multiple video benchmarks demonstrate that our approach sets a new state-of-the-art in zero-shot video depth estimation. We offer models of different scales to support a range of scenarios, with our smallest model capable of real-time performance at 30 FPS.
Key facts. Cited by 202 papers in the map (data through 7 Oct 2026). Builds on 36 of its 52 references that are in the map. First posted on arXiv 21 Jan 2025.
Builds on
36 of 52 references.
References · 36
- 2017Attention Is All You Needcited by 59,932
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
- 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
- 2021Vision Transformers for Dense Predictioncited by 1,789
- 2024Depth Anything V2cited by 1,385
- 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,346
- 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
- 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
- 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 646
- 2020AdaBins: Depth Estimation using Adaptive Binscited by 596
24 more
- 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 494
- 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 375
- 2023Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Imagecited by 357
- 2020Virtual KITTI 2cited by 345
- 2021GMFlow: Learning Optical Flow via Global Matchingcited by 324
- 2023ControlVideo: Training-free Controllable Text-to-Video Generationcited by 324
- 2024MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervisioncited by 290
- 2023PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Trackingcited by 274
- 2019DIODE: A Dense Indoor and Outdoor DEpth Datasetcited by 236
- 2020Consistent Video Depth Estimationcited by 220
- 2024DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videoscited by 202
- 2020Robust Consistent Video Depth Estimationcited by 188
- 2019ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residualscited by 180
- 2023MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimationcited by 173
- 2024ControlNeXt: Powerful and Efficient Control for Image and Video Generationcited by 158
- 2022NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimationcited by 151
- 2024Learning Temporally Consistent Video Depth from Video Diffusion Priorscited by 100
- 2022SimpleRecon: 3D Reconstruction Without 3D Convolutionscited by 83
- 2024Depth Any Video with Scalable Synthetic Datacited by 57
- 2021Consistent Depth of Moving Objects in Videocited by 56
- 2021Towards Real-Time Monocular Depth Estimation for Robotics: A Surveycited by 55
- 2022Less is More: Consistent Video Depth Estimation with Masked Frames Modelingcited by 30
- 2023MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimationcited by 24
- 2024Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matchingcited by 24
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