3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive
  Diffusion
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.
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Zhaoxi Chen
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2024-09-20T08:57:17.349Z
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Jiaxiang Tang
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2024-09-20T08:57:31.072Z
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Yuhao Dong
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2024-09-20T08:57:37.836Z
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Yuhao Dong
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Ziang Cao
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2024-09-20T08:57:44.215Z
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Ziang Cao
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Fangzhou Hong
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Fangzhou Hong
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Yushi Lan
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2024-09-20T08:58:00.887Z
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LAN YUSHI
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Tengfei Wang
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Tengfei Wang
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Haozhe Xie
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Haozhe Xie
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Tong Wu
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Tong Wu
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Shunsuke Saito
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Shunsuke Saito
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Liang Pan
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pan
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Dahua Lin
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Dahua Lin
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Ziwei Liu
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2024-09-20T08:59:18.972Z
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Ziwei Liu
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3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive
  Diffusion
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.
3DTopia-XL, a scalable 3D generative model using a new primitive-based representation and Diffusion Transformer framework, generates high-quality 3D assets with PBR materials and textures from textual or visual inputs.
3D generative models PrimX tensorial format PBR Diffusion Transformer Primitive Patch Compression Latent Primitive Diffusion