Avatar Concept Slider: Controllable Editing of Concepts in 3D Human Avatars

Lin Geng Foo, Yixuan He, Tiansong Zhou, Ajmal Saeed Mian, Hossein Rahmani, Jun Liu, Christian Theobalt

Keywords: 3D Human Avatar Editing, Controllable Editing

Abstract: Text-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 editing 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: Firstly, a Concept Sliding Loss based on linear discriminant analysis to pinpoint the concept-specific axes for precise editing. Secondly, an Attribute Preserving Loss based on principal component analysis for improved preservation of avatar identity during editing. We further propose 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 controllable 3D avatar editing, without compromising the avatar quality or its identifying attributes.

Primary Area: generative models

Submission Number: 2597