Object segmentation and object tracking are fundamental research areas in the computer
vision community. These two topics are difficult to handle some common challenges, such …

In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …

Synthesizing visual content that meets users' needs often requires flexible and precise
controllability of the pose, shape, expression, and layout of the generated objects. Existing …

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 …

For the last few decades, several major subfields of artificial intelligence including computer
vision, graphics, and robotics have progressed largely independently from each other …

Prior work for articulated 3D shape reconstruction often relies on specialized multi-view and
depth sensors or pre-built deformable 3D models. Such methods do not scale to diverse sets …

Existing deep learning based image inpainting methods use a standard convolutional
network over the corrupted image, using convolutional filter responses conditioned on both …

Tracking pixels in videos is typically studied as an optical flow estimation problem, where
every pixel is described with a displacement vector that locates it in the next frame. Even …

We present an unsupervised learning framework for simultaneously training single-view
depth prediction and optical flow estimation models using unlabeled video sequences …