Directional Texture Editing for 3D Models

• Published in: Computer graphics forum 2024-09, Vol.43 (6), p.n/a
• Main Authors: Liu, Shengqi, Chen, Zhuo, Gao, Jingnan, Yan, Yichao, Zhu, Wenhan, Lyu, Jiangjing, Yang, Xiaokang
• Format: Article
• Language: English
• Subjects: Editing; image and video processing; image/video editing; Optimization; rendering; Surface layers; Texts; Texture; texture synthesis; Three dimensional models
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.15196

Texture editing is a crucial task in 3D modelling that allows users to automatically manipulate the surface materials of 3D models. However, the inherent complexity of 3D models and the ambiguous text description lead to the challenge of this task. To tackle this challenge, we propose ITEM3D, a Texture Editing Model designed for automatic 3D object editing according to the text Instructions. Leveraging the diffusion models and the differentiable rendering, ITEM3D takes the rendered images as the bridge between text and 3D representation and further optimizes the disentangled texture and environment map. Previous methods adopted the absolute editing direction, namely score distillation sampling (SDS) as the optimization objective, which unfortunately results in noisy appearances and text inconsistencies. To solve the problem caused by the ambiguous text, we introduce a relative editing direction, an optimization objective defined by the noise difference between the source and target texts, to release the semantic ambiguity between the texts and images. Additionally, we gradually adjust the direction during optimization to further address the unexpected deviation in the texture domain. Qualitative and quantitative experiments show that our ITEM3D outperforms the state‐of‐the‐art methods on various 3D objects. We also perform text‐guided relighting to show explicit control over lighting. Our project page: https://shengqiliu1.github.io/ITEM3D/. ITEM3D: an efficient optimization method to edit the texture obedient to the text instruction. We introduce a relative editing direction, an optimization objective defined by the noise difference between the source and target texts, to release the semantic ambiguity between the texts and images. Additionally, we gradually adjust the direction during optimization to further address the unexpected deviation in the texture domain.