Category-Aware 3D Object Composition with Disentangled Texture and Shape Multi-view Diffusion

• URL: http://arxiv.org/abs/2509.02357v1
• Date: Tue, 02 Sep 2025 14:19:21 GMT
• Title: Category-Aware 3D Object Composition with Disentangled Texture and Shape Multi-view Diffusion
• Authors: Zeren Xiong, Zikun Chen, Zedong Zhang, Xiang Li, Ying Tai, Jian Yang, Jun Li,
• Abstract summary: We tackle a new task of 3D object synthesis, where a 3D model is composited with another object category to create a novel 3D model.<n>Most existing text/image/3D-to-3D methods struggle to effectively integrate multiple content sources.<n>We propose category+3D-to-3D (C33D), for generating novel and structurally coherent 3D models.
• Score: 31.888133775976414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we tackle a new task of 3D object synthesis, where a 3D model is composited with another object category to create a novel 3D model. However, most existing text/image/3D-to-3D methods struggle to effectively integrate multiple content sources, often resulting in inconsistent textures and inaccurate shapes. To overcome these challenges, we propose a straightforward yet powerful approach, category+3D-to-3D (C33D), for generating novel and structurally coherent 3D models. Our method begins by rendering multi-view images and normal maps from the input 3D model, then generating a novel 2D object using adaptive text-image harmony (ATIH) with the front-view image and a text description from another object category as inputs. To ensure texture consistency, we introduce texture multi-view diffusion, which refines the textures of the remaining multi-view RGB images based on the novel 2D object. For enhanced shape accuracy, we propose shape multi-view diffusion to improve the 2D shapes of both the multi-view RGB images and the normal maps, also conditioned on the novel 2D object. Finally, these outputs are used to reconstruct a complete and novel 3D model. Extensive experiments demonstrate the effectiveness of our method, yielding impressive 3D creations, such as shark(3D)-crocodile(text) in the first row of Fig. 1. A project page is available at: https://xzr52.github.io/C33D/

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