DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
- URL: http://arxiv.org/abs/2409.02095v1
- Date: Tue, 3 Sep 2024 17:52:03 GMT
- Title: DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
- Authors: Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, Ying Shan,
- Abstract summary: DepthCrafter generates temporally consistent long depth sequences with intricate details for open-world videos.
Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames.
DepthCrafter facilitates various downstream applications, including depth-based visual effects and conditional video generation.
- Score: 51.90501863934735
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Despite significant advancements in monocular depth estimation for static images, estimating video depth in the open world remains challenging, since open-world videos are extremely diverse in content, motion, camera movement, and length. We present DepthCrafter, an innovative method for generating temporally consistent long depth sequences with intricate details for open-world videos, without requiring any supplementary information such as camera poses or optical flow. DepthCrafter achieves generalization ability to open-world videos by training a video-to-depth model from a pre-trained image-to-video diffusion model, through our meticulously designed three-stage training strategy with the compiled paired video-depth datasets. Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets. We also propose an inference strategy that processes extremely long videos through segment-wise estimation and seamless stitching. Comprehensive evaluations on multiple datasets reveal that DepthCrafter achieves state-of-the-art performance in open-world video depth estimation under zero-shot settings. Furthermore, DepthCrafter facilitates various downstream applications, including depth-based visual effects and conditional video generation.
Related papers
- Depth Any Video with Scalable Synthetic Data [98.42356740981839]
We develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse synthetic environments.
We leverage the powerful priors of generative video diffusion models to handle real-world videos effectively.
Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.
arXiv Detail & Related papers (2024-10-14T17:59:46Z) - Learning Temporally Consistent Video Depth from Video Diffusion Priors [57.929828486615605]
This work addresses the challenge of video depth estimation.
We reformulate the prediction task into a conditional generation problem.
This allows us to leverage the prior knowledge embedded in existing video generation models.
arXiv Detail & Related papers (2024-06-03T16:20:24Z) - NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation [58.21817572577012]
Video depth estimation aims to infer temporally consistent depth.
We introduce NVDS+ that stabilizes inconsistent depth estimated by various single-image models in a plug-and-play manner.
We also elaborate a large-scale Video Depth in the Wild dataset, which contains 14,203 videos with over two million frames.
arXiv Detail & Related papers (2023-07-17T17:57:01Z) - MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to
Depth-aware Video Panoptic Segmentation [3.2489082010225494]
We propose a novel solution with a multi-task network that performs monocular depth estimation and video panoptic segmentation.
We introduce panoptic-guided depth losses and a novel panoptic masking scheme for moving objects to avoid corrupting the training signal.
arXiv Detail & Related papers (2022-10-14T07:00:42Z) - Consistent Depth of Moving Objects in Video [52.72092264848864]
We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera.
We formulate this objective in a new test-time training framework where a depth-prediction CNN is trained in tandem with an auxiliary scene-flow prediction over the entire input video.
We demonstrate accurate and temporally coherent results on a variety of challenging videos containing diverse moving objects (pets, people, cars) as well as camera motion.
arXiv Detail & Related papers (2021-08-02T20:53:18Z) - Video Depth Estimation by Fusing Flow-to-Depth Proposals [65.24533384679657]
We present an approach with a differentiable flow-to-depth layer for video depth estimation.
The model consists of a flow-to-depth layer, a camera pose refinement module, and a depth fusion network.
Our approach outperforms state-of-the-art depth estimation methods, and has reasonable cross dataset generalization capability.
arXiv Detail & Related papers (2019-12-30T10:45:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.