Geometric Style Transfer Method Overview
Figure 3 : Method overview. We first extract geometric style from reference images by training LoRA using a DreamBooth-style objective ( Sec. 3.1.1 ). We optimize per-face Jacobians to transfer geometric style to source mesh. The optimization is guided by SDS loss ( Sec. 3.1.2 ). To handle both large structural deformation and fine-grained details, we adapt a coarse-to-fine deformation ( Sec. 3.3 ), along with the cage-based regularization ( Sec. 3.2.1 ) and optional symmetry regularization ( Sec. 3.2.2 ).
论文上下文
Paper title: Image-Guided Geometric Stylization of 3D Meshes Abstract: Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual descriptions, and rarely supports bold geometric distortions beyond existing data distributions. We propose a geometric stylization framework that deforms a 3D mesh, allowing it to express the style of an image. While style is inherently ambiguous, we utilize pre-trained diffusion models to extract an abstract representation of the provided image. Our coarse-to-fine stylization pipeline can drastically deform the input 3D model to express a diverse range of geometric variations while retaining the valid topology of the original mesh and part-level semantics. We also propose an approximate VAE encoder that provides efficient and reliable gradients from mesh renderings. Extensive experiments demonstrate that our method can create stylized 3D meshes that reflect unique geometric features of the pictured assets, such as expressive poses and silhouettes, thereby supporting the creation of distinctive artistic 3D creations. Project page: https://changwoonchoi.github.io/GeoStyle Passages referencing this figure: roximate VAE encoder that provides efficient and reliable gradients from mesh renderings. Extensive experiments demonstrate that our method can create stylized 3D meshes that reflect unique geometric features of the pictured assets, such as expressive poses and silhouettes, thereby supporting the creation of distinctive artistic 3D creations. Project page: https://changwoonchoi.github.io/GeoStyle Figure 1 : Our method enables stylization of 3D meshes driven by image style references. The target stylized meshes retain the coarse structure and semantics of the input mesh while incorporating internal geometry derived from the style reference. * * footnotetext: Equal contribution. $\dagger$ $\dagger$ footnotetext: Young Min Kim is the corresponding author. 1 Introduction Despite impressive adv tistics [ 9 ] . Similarly, 3D stylization methods incorporate text descriptions to guide local geometric variations on the surface [ 41 , 6 ] , or produce specific geometric characteristics with handcrafted regularizations [ 34 , 33 , 23 ] . We expand the notion of style beyond high-frequency textures to embrace geometric features of various scales as components of a unique style. For example, in Fig. 1 , the distinctive silhouette of Bourgeois’s spider or the rigid structural characteristics of a fire hydrant cannot be described by local texture. Such a diverse range of variations requires a holistic analysis, whose geometric characteristics are challenging to describe unambiguously with an input text, as shown in Fig. 2 . We utilize reference images as a means of explicit description to
