3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive
  Diffusion
Authors: name: Zhaoxi Chen, name: Jiaxiang Tang, name: Yuhao Dong, name: Ziang Cao, name: Fangzhou Hong, name: Yushi Lan, name: Tengfei Wang, name: Haozhe Xie, name: Tong Wu, name: Shunsuke Saito, name: Liang Pan, name: Dahua Lin, name: Ziwei Liu

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.