发表机构
University of Wisconsin–Madison; Purdue University; Fujitsu Research of America（威斯康星大学麦迪逊分校; 普渡大学; 富士通美国研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SemanticSlider3D是一种无需训练的3D物体连续语义编辑技术，通过在先进3D生成模型潜空间构建语义编辑方向，在技术验证与用户研究中均表现优于基线方法，可作为现有3D创作工作流的有效补充。
AI 中文摘要
对3D物体连续语义属性的细粒度控制是3D内容创作的核心需求，但传统3D建模工作流或现有生成式AI工具的基于提示的交互均未提供良好支持。尽管基于滑块的方法在2D图像生成的细粒度语义控制中已被证实有效，但3D领域尚无等效方案。由于3D特有的几何完整性、跨视图一致性等挑战，将这些2D方法扩展至3D并非易事。我们提出SemanticSlider3D，一种无需针对特定属性训练的3D物体连续语义属性编辑技术。给定用户指定的属性，我们的流程在最先进的3D生成模型的潜空间中构建语义编辑方向，呈现出多样且一致的3D变化序列。在包含50组3D物体-属性对的数据集上开展的技术验证显示，在变化范围、一致性、3D物体质量及属性解耦性方面，我们的方法获得了全部5名人工评估者的偏好，优于结合2D滑块与图像转3D模型的基线方法。一项由6名参与者开展的探索性研究表明，SemanticSlider3D支持3D原型设计中的决策，且被认为是现有工作流的宝贵补充。
英文摘要
Fine-grained control over continuous semantic attributes of 3D objects is essential for 3D content creation, but is not well supported by conventional 3D modeling workflows or prompt-based interaction with existing generative AI tools. While slider-based methods have proven effective for fine-grained semantic control in 2D image generation, no equivalent approach exists for 3D. Extending these 2D methods to 3D is non-trivial due to challenges unique to 3D, including geometric integrity and cross-view coherence. We present SemanticSlider3D, a technique for continuous semantic attribute editing of 3D objects that requires no per-attribute training. Given a user-specified attribute, our pipeline constructs a semantic editing direction in the latent space of a state-of-the-art 3D generation model, presenting a diverse and coherent spectrum of 3D variations. A technical validation on a dataset of 50 3D object-attribute pairs shows our method was preferred by all five human assessors across variation range, consistency, 3D object quality, and attribute disentanglement, over a baseline combining a 2D slider with an image-to-3D model. An exploratory study with six participants demonstrates that SemanticSlider3D supported decision-making in 3D prototyping and was perceived as a valuable addition to existing workflows.
CommentsUIST 2026
