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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11006 |
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| _version_ | 1866917402104561664 |
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| author | Zhang, Zhiyuan Zhou, Zijian Li, Linjun Chen, Long Tang, Hao Gong, Yichen |
| author_facet | Zhang, Zhiyuan Zhou, Zijian Li, Linjun Chen, Long Tang, Hao Gong, Yichen |
| contents | 3D texture generation is receiving increasing attention, as it enables the creation of realistic and aesthetic texture materials for untextured 3D meshes. However, existing 3D texture generation methods are limited to producing only a few types of non-emissive PBR materials (e.g., albedo, metallic maps and roughness maps), making them difficult to replicate highly popular styles, such as cyberpunk, failing to achieve effects like realistic LED emissions. To address this limitation, we propose a novel task, emission texture generation, which enables the synthesized 3D objects to faithfully reproduce the emission materials from input reference images. Our key contributions include: first, We construct the Objaverse-Emission dataset, the first dataset that contains 40k 3D assets with high-quality emission materials. Second, we propose EmissionGen, a novel baseline for the emission texture generation task. Third, we define detailed evaluation metrics for the emission texture generation task. Our results demonstrate significant potential for future industrial applications. Dataset will be available at https://github.com/yx345kw/EmissionGen. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Realistic 3D Emission Materials: Dataset, Baseline, and Evaluation for Emission Texture Generation Zhang, Zhiyuan Zhou, Zijian Li, Linjun Chen, Long Tang, Hao Gong, Yichen Computer Vision and Pattern Recognition 3D texture generation is receiving increasing attention, as it enables the creation of realistic and aesthetic texture materials for untextured 3D meshes. However, existing 3D texture generation methods are limited to producing only a few types of non-emissive PBR materials (e.g., albedo, metallic maps and roughness maps), making them difficult to replicate highly popular styles, such as cyberpunk, failing to achieve effects like realistic LED emissions. To address this limitation, we propose a novel task, emission texture generation, which enables the synthesized 3D objects to faithfully reproduce the emission materials from input reference images. Our key contributions include: first, We construct the Objaverse-Emission dataset, the first dataset that contains 40k 3D assets with high-quality emission materials. Second, we propose EmissionGen, a novel baseline for the emission texture generation task. Third, we define detailed evaluation metrics for the emission texture generation task. Our results demonstrate significant potential for future industrial applications. Dataset will be available at https://github.com/yx345kw/EmissionGen. |
| title | Towards Realistic 3D Emission Materials: Dataset, Baseline, and Evaluation for Emission Texture Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.11006 |