  Semantic typographic logos harmoniously blend typeface and imagery to
represent semantic concepts while maintaining legibility. Conventional methods
using spatial composition and shape substitution are hindered by the
conflicting requirement for achieving seamless spatial fusion between
geometrically dissimilar typefaces and semantics. While recent advances made AI
generation of semantic typography possible, the end-to-end approaches exclude
designer involvement and disregard personalized design. This paper presents
TypeDance, an AI-assisted tool incorporating design rationales with the
generative model for personalized semantic typographic logo design. It
leverages combinable design priors extracted from uploaded image exemplars and
supports type-imagery mapping at various structural granularity, achieving
diverse aesthetic designs with flexible control. Additionally, we instantiate a
comprehensive design workflow in TypeDance, including ideation, selection,
generation, evaluation, and iteration. A two-task user evaluation, including
imitation and creation, confirmed the usability of TypeDance in design across
different usage scenarios
