We introduce CoTracker, a transformer-based model that tracks a large number of 2D points
in long video sequences. Differently from most existing approaches that track points …

Learning-based optical flow estimation has been dominated with the pipeline of cost volume
with convolutions for flow regression, which is inherently limited to local correlations and …

We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

Optical flow is an indispensable building block for various important computer vision tasks,
including motion estimation, object tracking, and disparity measurement. In this work, we …

To reduce the human efforts in neural network design, Neural Architecture Search (NAS)
has been applied with remarkable success to various high-level vision tasks such as …

Optical flow estimation aims to find the 2D motion field by identifying corresponding pixels
between two images. Despite the tremendous progress of deep learning-based optical flow …

Optical flow estimation is a fundamental task in computer vision. Recent direct-regression
methods using deep neural networks achieve remarkable performance improvement …

Full-motion cost volumes play a central role in current state-of-the-art optical flow methods.
However, constructed using simple feature correlations, they lack the ability to encapsulate …

Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to
achieve effective multi-modal learning. In this paper, we introduce a novel multi-stage …

In this paper, we propose a simple yet effective approach for Tracking Any Point with
TRansformers (TAPTR). Based on the observation that point tracking bears a great …