Abstract Optical Flow Estimation is an essential component for many image processing
techniques. This field of research in computer vision has seen an amazing development in …

Deep generative models allow for photorealistic image synthesis at high resolutions. But for
many applications, this is not enough: content creation also needs to be controllable. While …

Most recent works in optical flow extraction focus on the accuracy and neglect the time
complexity. However, in real-life visual applications, such as tracking, activity detection and …

Learning optical flow with neural networks is hampered by the need for obtaining training
data with associated ground truth. Unsupervised learning is a promising direction, yet the …

We present an optical flow estimation approach that operates on the full four-dimensional
cost volume. This direct approach shares the structural benefits of leading stereo matching …

Modern large displacement optical flow algorithms usually use an initialization by either
sparse descriptor matching techniques or dense approximate nearest neighbor fields. While …

As a key component in many computer vision systems, optical flow estimation, especially
with large displacements, remains an open problem. In this paper we present a simple but …

Optical flow is inherently a 2D search problem, and thus the computational complexity grows
quadratically with respect to the search window, making large displacements matching …

Existing optical flow methods make generic, spatially homogeneous, assumptions about the
spatial structure of the flow. In reality, optical flow varies across an image depending on …

We present a global optimization approach to optical flow estimation. The approach
optimizes a classical optical flow objective over the full space of map**s between discrete …