We propose "Shape from Semantics", which is able to create 3D models whose geometry and appearance match given semantics when observed from different views. Traditional "Shape from X" tasks usually use visual input (e.g., RGB images or depth maps) to reconstruct geometry, imposing strict constraints that limit creative explorations. As applications, works like Shadow Art and Wire Art often struggle to grasp the embedded semantics of their design through direct observation and rely heavily on specific setups for proper display. To address these limitations, our framework uses semantics as input, greatly expanding the design space to create objects that integrate multiple semantic elements and are easily discernible by observers. Considering that this task requires a rich imagination, we adopt various generative models and structure-to-detail pipelines. Specifically, we adopt multi-semantics Score Distillation Sampling (SDS) to distill 3D geometry and appearance from 2D diffusion models, ensuring that the initial shape is consistent with the semantic input. We then use image restoration and video generation models to add more details as supervision. Finally, we introduce neural signed distance field (SDF) representation to achieve detailed shape reconstruction. Our framework generates meshes with complex details, well-structured geometry, coherent textures, and smooth transitions, resulting in visually appealing and eye-catching designs.