TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling

• URL: http://arxiv.org/abs/2408.01291v1
• Date: Fri, 2 Aug 2024 14:24:40 GMT
• Title: TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling
• Authors: Dong Huo, Zixin Guo, Xinxin Zuo, Zhihao Shi, Juwei Lu, Peng Dai, Songcen Xu, Li Cheng, Yee-Hong Yang,
• Abstract summary: We present TexGen, a novel multi-view sampling and resampling framework for texture generation. Our proposed method produces significantly better texture quality for diverse 3D objects with a high degree of view consistency. Our proposed texture generation technique can also be applied to texture editing while preserving the original identity.
• Score: 37.67373829836975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual descriptions. Current methods for generating and assembling textures from sampled views often result in prominent seams or excessive smoothing. To tackle these issues, we present TexGen, a novel multi-view sampling and resampling framework for texture generation leveraging a pre-trained text-to-image diffusion model. For view consistent sampling, first of all we maintain a texture map in RGB space that is parameterized by the denoising step and updated after each sampling step of the diffusion model to progressively reduce the view discrepancy. An attention-guided multi-view sampling strategy is exploited to broadcast the appearance information across views. To preserve texture details, we develop a noise resampling technique that aids in the estimation of noise, generating inputs for subsequent denoising steps, as directed by the text prompt and current texture map. Through an extensive amount of qualitative and quantitative evaluations, we demonstrate that our proposed method produces significantly better texture quality for diverse 3D objects with a high degree of view consistency and rich appearance details, outperforming current state-of-the-art methods. Furthermore, our proposed texture generation technique can also be applied to texture editing while preserving the original identity. More experimental results are available at https://dong-huo.github.io/TexGen/

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