Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. However, current Generative Art (GenArt) evaluators remain fixated on surface-level image quality or literal prompt adherence, failing to assess the deeper symbolic or abstract meaning intended by the creator. We address this gap by formalizing a Peircean computational semiotic theory that models Human-GenArt Interaction (HGI) as cascaded semiosis. This framework reveals that artistic meaning is conveyed through three modes - iconic, symbolic, and indexical - yet existing evaluators operate heavily within the iconic mode, remaining structurally blind to the latter two. To overcome this structural blindness, we propose SemJudge. This evaluator explicitly assesses symbolic and indexical meaning in HGI via a Hierarchical Semiosis Graph (HSG) that reconstructs the meaning-making process from prompt to generated artifact. Extensive quantitative experiments show that SemJudge aligns more closely with human judgments than prior evaluators on an interpretation-intensive fine-art benchmark. User studies further demonstrate that SemJudge produces deeper, more insightful artistic interpretations, thereby paving the way for GenArt to move beyond the generation of "pretty" images toward a medium capable of expressing complex human experience. Project page: https://github.com/songrise/SemJudge.

翻译：阐释对于破解艺术语言至关重要：观众通过从视觉作品中还原意义来实现与艺术家的沟通。然而，当前的生成艺术评估方法仍局限于表层图像质量或文字提示的忠实度，未能评估创作者意图传达的深层符号或抽象意义。为弥补这一不足，我们形式化了皮尔士计算符号学理论，将人机生成艺术交互建模为级联符号过程。该框架揭示了艺术意义通过三种模式传递——图像性、象征性和指示性——但现有评估方法主要运行于图像性模式，在结构上对后两种模式存在盲区。为克服这种结构性盲区，我们提出SemJudge评估器。该评估器通过层级符号图显式评估人机生成艺术交互中的象征性与指示性意义，该图重构了从提示到生成作品的意义构建过程。大量定量实验表明，在阐释密集型的精美艺术基准测试中，SemJudge比先前评估器更贴近人类判断。用户研究进一步证明，SemJudge能产生更深刻、更具洞见的艺术阐释，从而为生成艺术超越“漂亮”图像生成、迈向能够表达复杂人类体验的媒介铺平道路。项目页面：https://github.com/songrise/SemJudge。