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.
Abstract (translated)
尽管静态图像中单目深度估计取得了显著的进步，但在开放世界中估计视频深度仍然具有挑战性，因为开放世界视频的内容、运动、相机运动和长度差异极大。我们提出了一种创新的方法：DepthCrafter，用于生成具有复杂细节的时效性一致长深度序列，无需要求任何补充信息，如相机姿态或光流。通过精心设计的三个阶段的训练策略，DepthCrafter从预训练的图像-到视频扩散模型中训练视频到深度模型，实现了对开放世界视频的泛化能力。我们的训练方法使模型能够在同一时间生成不同长度的深度序列，多达110帧，并从现实和合成数据集中收获精确的深度细节和丰富的内容多样性。我们还提出了一个处理非常长视频的推理策略，通过逐帧估计和无缝拼接。在多个数据集上的全面评估显示，DepthCrafter在零散设置下实现了开放世界视频深度估计的卓越性能。此外，DepthCrafter还有助于各种下游应用，包括基于深度的视觉效果和条件视频生成。
URL
https://arxiv.org/abs/2409.02095
