title: Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured
    3D Assets
summary: While generative artificial intelligence has advanced significantly across
  text, image, audio, and video domains, 3D generation remains comparatively
  underdeveloped due to fundamental challenges such as data scarcity, algorithmic
  limitations, and ecosystem fragmentation. To this end, we present Step1X-3D, an
  open framework addressing these challenges through: (1) a rigorous data
  curation pipeline processing >5M assets to create a 2M high-quality dataset
  with standardized geometric and textural properties; (2) a two-stage 3D-native
  architecture combining a hybrid VAE-DiT geometry generator with an
  diffusion-based texture synthesis module; and (3) the full open-source release
  of models, training code, and adaptation modules. For geometry generation, the
  hybrid VAE-DiT component produces TSDF representations by employing
  perceiver-based latent encoding with sharp edge sampling for detail
  preservation. The diffusion-based texture synthesis module then ensures
  cross-view consistency through geometric conditioning and latent-space
  synchronization. Benchmark results demonstrate state-of-the-art performance
  that exceeds existing open-source methods, while also achieving competitive
  quality with proprietary solutions. Notably, the framework uniquely bridges the
  2D and 3D generation paradigms by supporting direct transfer of 2D control
  techniques~(e.g., LoRA) to 3D synthesis. By simultaneously advancing data
  quality, algorithmic fidelity, and reproducibility, Step1X-3D aims to establish
  new standards for open research in controllable 3D asset generation.
paper_authors._id: 6822b668a307d0f1bf9bb4e0
paper_authors.name: Weiyu Li
paper_authors._id: 6822b668a307d0f1bf9bb4e1
paper_authors.name: Xuanyang Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: xuanyang zhang
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paper_authors.user: xuanyangz
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paper_authors.name: Zheng Sun
paper_authors._id: 6822b668a307d0f1bf9bb4e3
paper_authors.name: Di Qi
paper_authors._id: 6822b668a307d0f1bf9bb4e4
paper_authors.name: Hao Li
paper_authors._id: 6822b668a307d0f1bf9bb4e5
paper_authors.name: Wei Cheng
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paper_authors.fullname: Wei Cheng
paper_authors.type: user
paper_authors.user: wchengad
paper_authors._id: 6822b668a307d0f1bf9bb4e6
paper_authors.name: Weiwei Cai
paper_authors._id: 6822b668a307d0f1bf9bb4e7
paper_authors.name: Shihao Wu
paper_authors.status: admin_assigned
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paper_authors._id: 67dd1a0968dc64637481ec72
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/hQvTErvt9-rIsMPxZFwq1.png
paper_authors.fullname: shihao wu
paper_authors.type: user
paper_authors.user: shian7
paper_authors._id: 6822b668a307d0f1bf9bb4e8
paper_authors.name: Jiarui Liu
paper_authors._id: 6822b668a307d0f1bf9bb4e9
paper_authors.name: Zihao Wang
paper_authors._id: 6822b668a307d0f1bf9bb4ea
paper_authors.name: Xiao Chen
paper_authors._id: 6822b668a307d0f1bf9bb4eb
paper_authors.name: Feipeng Tian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-13T08:29:33.047Z
paper_authors._id: 65fbe6c7ae01a24b40f6d0a5
paper_authors.avatarUrl: /avatars/3f396e5623dcdbe11721744138245ee7.svg
paper_authors.fullname: Fei-Peng Tian
paper_authors.type: user
paper_authors.user: flybirdtian
paper_authors._id: 6822b668a307d0f1bf9bb4ec
paper_authors.name: Jianxiong Pan
paper_authors.status: admin_assigned
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paper_authors.fullname: PanJianxiong
paper_authors.type: user
paper_authors.user: PanJianxiong
paper_authors._id: 6822b668a307d0f1bf9bb4ed
paper_authors.name: Zeming Li
paper_authors._id: 6822b668a307d0f1bf9bb4ee
paper_authors.name: Gang Yu
paper_authors.status: claimed_verified
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paper_authors.fullname: Gang Yu
paper_authors.type: user
paper_authors.user: skicy
paper_authors._id: 6822b668a307d0f1bf9bb4ef
paper_authors.name: Xiangyu Zhang
paper_authors._id: 6822b668a307d0f1bf9bb4f0
paper_authors.name: Daxin Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-13T08:30:25.055Z
paper_authors._id: 60d4440fe648443279aaffd8
paper_authors.avatarUrl: /avatars/bf7209c1f14ae120f5bfda5fda1301b7.svg
paper_authors.fullname: Daxin Jiang
paper_authors.type: user
paper_authors.user: djiang
paper_authors._id: 6822b668a307d0f1bf9bb4f1
paper_authors.name: Ping Tan
paper_title: Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured
    3D Assets
paper_summary: While generative artificial intelligence has advanced significantly across
  text, image, audio, and video domains, 3D generation remains comparatively
  underdeveloped due to fundamental challenges such as data scarcity, algorithmic
  limitations, and ecosystem fragmentation. To this end, we present Step1X-3D, an
  open framework addressing these challenges through: (1) a rigorous data
  curation pipeline processing >5M assets to create a 2M high-quality dataset
  with standardized geometric and textural properties; (2) a two-stage 3D-native
  architecture combining a hybrid VAE-DiT geometry generator with an
  diffusion-based texture synthesis module; and (3) the full open-source release
  of models, training code, and adaptation modules. For geometry generation, the
  hybrid VAE-DiT component produces TSDF representations by employing
  perceiver-based latent encoding with sharp edge sampling for detail
  preservation. The diffusion-based texture synthesis module then ensures
  cross-view consistency through geometric conditioning and latent-space
  synchronization. Benchmark results demonstrate state-of-the-art performance
  that exceeds existing open-source methods, while also achieving competitive
  quality with proprietary solutions. Notably, the framework uniquely bridges the
  2D and 3D generation paradigms by supporting direct transfer of 2D control
  techniques~(e.g., LoRA) to 3D synthesis. By simultaneously advancing data
  quality, algorithmic fidelity, and reproducibility, Step1X-3D aims to establish
  new standards for open research in controllable 3D asset generation.
paper_ai_summary: Step1X-3D addresses challenges in 3D generation through a hybrid VAE-DiT geometry generator and diffusion-based texture synthesis, achieving state-of-the-art results with an open-source framework.
paper_ai_keywords: hybrid VAE-DiT diffusion-based texture synthesis TSDF representations perceiver-based latent encoding sharp edge sampling cross-view consistency geometric conditioning latent-space synchronization LoRA 3D-native architecture