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3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion

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arxiv 2409.12957 v2 pith:6WVE7WPP submitted 2024-09-19 cs.CV cs.GR

3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion

Zhaoxi Chen , Jiaxiang Tang , Yuhao Dong , Ziang Cao , Fangzhou Hong , Yushi Lan , Tengfei Wang , Haozhe Xie
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Tong Wu Shunsuke Saito Liang Pan Dahua Lin Ziwei Liu
This is my paper
classification cs.CV cs.GR
keywords assetsdtopia-xlgenerativehigh-qualitydiffusionprimitiveexistingmethods
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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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Cited by 9 Pith papers

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    cs.CV 2026-07 unverdicted novelty 6.0

    Ink3D decouples geometry from texture by generating dense orbit videos with a conditional video model and baking them via a neural optimizer to produce complex 3D textures.

  3. AnySurf: Any Surface Generation with Directed Edge

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    AnySurf generates open, closed and hybrid 3D surfaces with accurate normals via directed-edge enhanced FDG-D, a lightweight DE-Adapter, ROS-FT post-training, and the Outfit3D dataset.

  4. PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects

    cs.CV 2026-05 unverdicted novelty 6.0

    PhysX-Omni unifies simulation-ready 3D asset generation across rigid, deformable, and articulated objects via a new geometry representation, the PhysXVerse dataset, and the PhysX-Bench evaluation suite.

  5. Native and Compact Structured Latents for 3D Generation

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