5.2. Time for Some Analysis

Time and Space. We begin by analyzing the use of both
time and space and validate the importance of conditioning
our mapper on both input types. Specifically, we consider
three variants of our mapper: (1) a space-conditioned map-
per; (2) a time-conditioned mapper; and (3) a mapper that
is not conditioned on either. For the final variant, we sim-
ply pass a fixed input to the neural network and optimize
the network parameters as is done in our standard training
scheme. Sample reconstructions of each variant using the
prompt “A photo of S∗” are provided in Figure 10. As can
be seen, conditioning our neural mapper on both time and
space is crucial for capturing fine-level details.
A Neural Space-Time Representation for Text-to-Image Personalization