title: 3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive
    Diffusion
summary: 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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paper_title: 3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive
    Diffusion
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 3D generative models PrimX tensorial format PBR Diffusion Transformer Primitive Patch Compression Latent Primitive Diffusion