Language based editing of 3D human avatars to precisely match user requirements is challenging due to the inherent ambiguity and limited expressiveness of natural language. To overcome this, we propose the Avatar Concept Slider (ACS), a 3D avatar editing method that allows precise manipulation of semantic concepts in human avatars towards a specified intermediate point between two extremes of concepts, akin to moving a knob along a slider track. To achieve this, our ACS has three designs. 1) A Concept Sliding Loss based on Linear Discriminant Analysis to pinpoint the concept-specific axis for precise editing. 2) An Attribute Preserving Loss based on Principal Component Analysis for improved preservation of avatar identity during editing. 3) A 3D Gaussian Splatting primitive selection mechanism based on concept-sensitivity, which updates only the primitives that are the most sensitive to our target concept, to improve efficiency. Results demonstrate that our ACS enables fine-grained 3D avatar editing with efficient feedback, without harming the avatar quality or compromising the avatar's identifying attributes.
基于语言对3D人类头像进行编辑以精确匹配用户需求是一项具有挑战性的任务，因为自然语言固有的模糊性和表达限制。为了解决这个问题，我们提出了Avatar Concept Slider (ACS)，这是一种3D头像编辑方法，允许在两个概念极端之间向指定的中间点精确操作语义概念，类似于在滑块轨道上移动旋钮。为实现这一目标，我们的ACS有三个设计要点：1) 基于线性判别分析的概念滑动损失，用于精确定位概念特定轴线以进行编辑。2) 基于主成分分析的属性保留损失，以在编辑过程中提高头像身份的保留。3) 基于概念敏感性的3D高斯斑点原语选择机制，仅更新对目标概念最敏感的原语，以提高效率。结果表明，我们的ACS实现了细粒度的3D头像编辑，并且能够高效地提供反馈，而不会损害头像质量或妨碍头像的识别属性。