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 …

We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural
network architecture for learning optical flow. FlowFormer tokenizes the 4D cost volume built …

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 …

FlowFormer introduces a transformer architecture into optical flow estimation and achieves
state-of-the-art performance. The core component of FlowFormer is the transformer-based …

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 …

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to
previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently …

Abstract We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for
video frame interpolation. It is based on two essential designs. First, we build bidirectional …

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 …

Optical flow is a fundamental method used for quantitative motion estimation on the image
plane. In the deep learning era, most works treat it as a task of'matching of features', learning …