3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910876369420288 |
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| author | Chen, Zhaoxi Tang, Jiaxiang Dong, Yuhao Cao, Ziang Hong, Fangzhou Lan, Yushi Wang, Tengfei Xie, Haozhe Wu, Tong Saito, Shunsuke Pan, Liang Lin, Dahua Liu, Ziwei |
| author_facet | Chen, Zhaoxi Tang, Jiaxiang Dong, Yuhao Cao, Ziang Hong, Fangzhou Lan, Yushi Wang, Tengfei Xie, Haozhe Wu, Tong Saito, Shunsuke Pan, Liang Lin, Dahua Liu, Ziwei |
| contents | The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the lack of assets for physically based rendering (PBR). In this paper, we introduce 3DTopia-XL, a scalable native 3D generative model designed to overcome these limitations. 3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which encodes detailed shape, albedo, and material field into a compact tensorial format, facilitating the modeling of high-resolution geometry with PBR assets. On top of the novel representation, we propose a generative framework based on Diffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2) and Latent Primitive Diffusion. 3DTopia-XL learns to generate high-quality 3D assets from textual or visual inputs. We conduct extensive qualitative and quantitative experiments to demonstrate that 3DTopia-XL significantly outperforms existing methods in generating high-quality 3D assets with fine-grained textures and materials, efficiently bridging the quality gap between generative models and real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion Chen, Zhaoxi Tang, Jiaxiang Dong, Yuhao Cao, Ziang Hong, Fangzhou Lan, Yushi Wang, Tengfei Xie, Haozhe Wu, Tong Saito, Shunsuke Pan, Liang Lin, Dahua Liu, Ziwei Computer Vision and Pattern Recognition Graphics The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the lack of assets for physically based rendering (PBR). In this paper, we introduce 3DTopia-XL, a scalable native 3D generative model designed to overcome these limitations. 3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which encodes detailed shape, albedo, and material field into a compact tensorial format, facilitating the modeling of high-resolution geometry with PBR assets. On top of the novel representation, we propose a generative framework based on Diffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2) and Latent Primitive Diffusion. 3DTopia-XL learns to generate high-quality 3D assets from textual or visual inputs. We conduct extensive qualitative and quantitative experiments to demonstrate that 3DTopia-XL significantly outperforms existing methods in generating high-quality 3D assets with fine-grained textures and materials, efficiently bridging the quality gap between generative models and real-world applications. |
| title | 3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2409.12957 |