无需训练即可对3D物体进行连续语义编辑，像滑块调色一样自由调整形状属性。
SemanticSlider3D: Training-Free Continuous Semantic Editing for 3D Objects
- 通过构建潜在空间中的语义方向，实现零训练的3D语义编辑。
- 5位评估者全部偏好该方法，其生成的3D变化范围更广、一致性更高。
- 适合3D设计、原型制作人员，提升交互式创作效率。
对3D物体的细粒度语义属性进行可控编辑对3D内容创作至关重要，但传统建模流程和现有生成式AI工具难以支持。尽管2D图像生成中滑块式控制已证明有效，但3D领域尚无类似方法。将2D技术扩展至3D面临几何完整性与跨视角一致性等挑战。本文提出SemanticSlider3D，一种无需每属性训练的3D语义连续编辑技术。给定用户指定属性，该方法在先进3D生成模型的潜在空间中构建语义编辑方向，生成多样且一致的3D变化。在包含50个3D物体-属性对的数据集上，5名人类评估者均偏好本方法，在变化范围、一致性、3D质量与属性解耦性方面优于结合2D滑块与图像到3D模型的基线方法。六名参与者的探索性研究显示，该方法有效支持3D原型设计决策，被视作现有工作流的有力补充。
原文摘要 · Abstract (English)
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.
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