  We propose an approach for the generation of topology-optimized structures
with text-guided appearance stylization. This methodology aims to enrich the
concurrent design of a structure's physical functionality and aesthetic
appearance. Users can effortlessly input descriptive text to govern the style
of the structure. Our system employs a hash-encoded neural network as the
implicit structure representation backbone, which serves as the foundation for
the co-optimization of structural mechanical performance, style, and
connectivity, to ensure full-color, high-quality 3D-printable solutions. We
substantiate the effectiveness of our system through extensive comparisons,
demonstrations, and a 3D printing test.
