  Every time you sit in front of a TV or monitor, your face is actively
illuminated by time-varying patterns of light. This paper proposes to use this
time-varying illumination for synthetic relighting of your face with any new
illumination condition. In doing so, we take inspiration from the light stage
work of Debevec et al., who first demonstrated the ability to relight people
captured in a controlled lighting environment. Whereas existing light stages
require expensive, room-scale spherical capture gantries and exist in only a
few labs in the world, we demonstrate how to acquire useful data from a normal
TV or desktop monitor. Instead of subjecting the user to uncomfortable rapidly
flashing light patterns, we operate on images of the user watching a YouTube
video or other standard content. We train a deep network on images plus monitor
patterns of a given user and learn to predict images of that user under any
target illumination (monitor pattern). Experimental evaluation shows that our
method produces realistic relighting results. Video results are available at
http://grail.cs.washington.edu/projects/Light_Stage_on_Every_Desk/.
