Uploaded June 2020 | Updated September 2026, 8 hours ago
The Reality Lab Lectures - Tuesday, April 21, 2020
TALK TITLE: Impossible outside Virtual Reality
SPEAKER: Mar Gonzalez Franco (Senior Researcher, Microsoft)
RECORDING: Event held over videoconference (during the COVID-19 shutdown) and recorded by UW CSE Production Team
TALK ABSTRACT: Virtual Reality and Augmented Reality to become the primary device for interaction with digital content, beyond the form factor, we need to understand what types of things we can do in VR that would be impossible with other technologies. That is, what does spatial computing bring to the table. For once, the spatialization of our senses. We can enhance audio or proprioception in complete new ways. We can grab and touch objects with new controllers, like never before. Even in the empty space between our hands. But how fast do we adapt to the new sensory experiences? Avatars are also unique to VR. They represent other humans but can also substitute our own bodies. And we perceive our world through our bodies. Hence avatars also change our perception of our surroundings. In this presentation we will explore the uniqueness of VR, from perception to avatars and how they can ultimately change our behavior and interactions with the digital content.
© UW Reality Lab, 2020
http://realitylab.uw.edu
The Reality Lab Lectures - Tuesday, April 21, 2020
TALK TITLE: Impossible outside Virtual Reality
SPEAKER: Mar Gonzalez Franco (Senior Researcher, Microsoft)
RECORDING: Event held over videoconference (during the COVID-19 shutdown) and recorded by UW CSE Production Team
TALK ABSTRACT: Virtual Reality and Augmented Reality to become the primary device for interaction with digital content, beyond the form factor, we need to understand what types of things we can do in VR that would be impossible with other technologies. That is, what does spatial computing bring to the table. For once, the spatialization of our senses. We can enhance audio or proprioception in complete new ways. We can grab and touch objects with new controllers, like never before. Even in the empty space between our hands. But how fast do we adapt to the new sensory experiences? Avatars are also unique to VR. They represent other humans but can also substitute our own bodies. And we perceive our world through our bodies. Hence avatars also change our perception of our surroundings. In this presentation we will explore the uniqueness of VR, from perception to avatars and how they can ultimately change our behavior and interactions with the digital content.
© UW Reality Lab, 2020
http://realitylab.uw.edu





![Teaser Video: Lifespan Age Transformation Synthesis
VIDEO: 60-second Teaser video [FULL project video: https://youtu.be/9fulnt2_q_Y]
TITLE: Lifespan Age Transformation Synthesis
AUTHORS: Roy Or-El, Soumyadip Sengupta, Ohad Fried, Eli Shechtman, Ira Kemelmacher-Shlizerman
ABSTRACT: We address the problem of single photo age progression and regression the prediction of how a person might look in the future, or how they looked in the past. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. This limits the applicability of previous methods to aging of adults to slightly older adults, and application of those methods to photos of children does not produce quality results. We propose a novel multi-domain image-to-image generative adversarial network architecture, whose learned latent space models a continuous bi-directional aging process. The network is trained on the FFHQ dataset, which we labeled for ages, gender, and semantic segmentation. Fixed age classes are used as anchors to approximate continuous age transformation. Our framework can predict a full head portrait for ages 0 70 from a single photo, modifying both texture and shape of the head. We demonstrate results on a wide variety of photos and datasets, and show significant improvement over the state of the art.
CODE: https://github.com/royorel/Lifespan_Age_Transformation_Synthesis
COLAB DEMO: https://colab.research.google.com/github/royorel/Lifespan_Age_Transformation_Synthesis/blob/master/LATS_demo.ipynb
DATA: https://github.com/royorel/FFHQ-Aging-Dataset
PROJECT PAGE: http://grail.cs.washington.edu/projects/lifespan_age_transformation_synthesis/ Teaser Video: Lifespan Age Transformation Synthesis](https://i.ytimg.com/vi/_jTFcjN2hBk/mqdefault.jpg)




