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Architext: Language-Driven Generative Architecture Design
We conduct a thorough quantitative evaluation of Architext's downstream task performance, focusing on semantic accuracy and diversity for a number of pre-trained language models ranging from 120 million to 6 billion parameters.
AffectGAN: Affect-Based Generative Art Driven by Semantics
This paper introduces a novel method for generating artistic images that express particular affective states.
ARCH-Elites: Quality-Diversity for Urban Design
This paper introduces ARCH-Elites, a MAP-Elites implementation that can reconfigure large-scale urban layouts at real-world locations via a pre-trained surrogate model instead of costly simulations.
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