Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

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 present a new test-time optimization method for estimating dense and long-range motion
from a video sequence. Prior optical flow or particle video tracking algorithms typically …

We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework,
for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to …

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 …

A central goal of machine learning is the development of systems that can solve many
problems in as many data domains as possible. Current architectures, however, cannot be …

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 …