I'm interested in the motion of the visual world. Motion is critical for intelligent systems. As Gibson nicely put it "We see in order to move and we move in order to see". Motion informs us of changes in the world, its structure, physical properties, etc. In our lab we study what motion tells us about the world and how to estimate it from video.
These concepts often fall in the following Computer Vision problem buckets:
These problems often require a fundamental ability to learn. When we have lots of high quality labeled data, visual problems may not be so daunting. When we have only some supervision, like it's the case of videos, learning becomes harder. These learning challenges broadly fall in the following Machine Learning areas:
Below is recent work organized by category, and my full list of publications can be found here.
Sevilla-Lara, Sun, Jampani and Black, "Optical Flow with Semantic Segmentation and Localized Layers", in CVPR 2016
Wulff, Sevilla-Lara, Black, "Optical Flow in Mostly Rigid Scenes", in CVPR 2017
Shou, Yan, Kalantidis, Sevilla-Lara, et al. "DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition" in CVPR 2019 (to appear).
Sevilla-Lara, Liao, Guney, Jampani, Geiger and Black, "On the Integration of Optical Flow and Action Recognition", in GCPR 2018
Over the years I have been very fortunate to work together and learn from a big group of collaborators: