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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.04786 |
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| _version_ | 1866915653825331200 |
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| author | Chen, Chia-Hao Zou, Zi-Xin Cao, Yan-Pei Yuan, Ze Luo, Guan Qi, Xiaojuan Liang, Ding Zhang, Song-Hai Guo, Yuan-Chen |
| author_facet | Chen, Chia-Hao Zou, Zi-Xin Cao, Yan-Pei Yuan, Ze Luo, Guan Qi, Xiaojuan Liang, Ding Zhang, Song-Hai Guo, Yuan-Chen |
| contents | Generating high-fidelity, seamless textures directly on 3D surfaces, what we term 3D-native texturing, remains a fundamental open challenge, with the potential to overcome long-standing limitations of UV-based and multi-view projection methods. However, existing native approaches are constrained by the absence of a powerful and versatile latent representation, which severely limits the fidelity and generality of their generated textures. We identify this representation gap as the principal barrier to further progress. We introduce LaFiTe, a framework that addresses this challenge by learning to generate textures as a 3D generative sparse latent color field. At its core, LaFiTe employs a variational autoencoder (VAE) to encode complex surface appearance into a sparse, structured latent space, which is subsequently decoded into a continuous color field. This representation achieves unprecedented fidelity, exceeding state-of-the-art methods by >10 dB PSNR in reconstruction, by effectively disentangling texture appearance from mesh topology and UV parameterization. Building upon this strong representation, a conditional rectified-flow model synthesizes high-quality, coherent textures across diverse styles and geometries. Extensive experiments demonstrate that LaFiTe not only sets a new benchmark for 3D-native texturing but also enables flexible downstream applications such as material synthesis and texture super-resolution, paving the way for the next generation of 3D content creation workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LaFiTe: A Generative Latent Field for 3D Native Texturing Chen, Chia-Hao Zou, Zi-Xin Cao, Yan-Pei Yuan, Ze Luo, Guan Qi, Xiaojuan Liang, Ding Zhang, Song-Hai Guo, Yuan-Chen Computer Vision and Pattern Recognition Generating high-fidelity, seamless textures directly on 3D surfaces, what we term 3D-native texturing, remains a fundamental open challenge, with the potential to overcome long-standing limitations of UV-based and multi-view projection methods. However, existing native approaches are constrained by the absence of a powerful and versatile latent representation, which severely limits the fidelity and generality of their generated textures. We identify this representation gap as the principal barrier to further progress. We introduce LaFiTe, a framework that addresses this challenge by learning to generate textures as a 3D generative sparse latent color field. At its core, LaFiTe employs a variational autoencoder (VAE) to encode complex surface appearance into a sparse, structured latent space, which is subsequently decoded into a continuous color field. This representation achieves unprecedented fidelity, exceeding state-of-the-art methods by >10 dB PSNR in reconstruction, by effectively disentangling texture appearance from mesh topology and UV parameterization. Building upon this strong representation, a conditional rectified-flow model synthesizes high-quality, coherent textures across diverse styles and geometries. Extensive experiments demonstrate that LaFiTe not only sets a new benchmark for 3D-native texturing but also enables flexible downstream applications such as material synthesis and texture super-resolution, paving the way for the next generation of 3D content creation workflows. |
| title | LaFiTe: A Generative Latent Field for 3D Native Texturing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.04786 |