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pith:SHXQOHK4

pith:2025:SHXQOHK45WRRHEFQBAL2WGLEIH
not attested not anchored not stored refs resolved

Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

Bojian Zheng, Bowen Zhang, Chao Zhang, Chunchao Guo, Di Luo, Di Wang, Dongyuan Guo, Haolin Liu, Hao Zhang, Hongyu Yan, Huiwen Shi, Jiaao Yu, Jianchen Zhu, Jie Jiang, Jihong Zhang, Jingwei Huang, Junlin Yu, Kai Liu, Liang Dong, Lifu Wang, Lin Niu, Linus, Meng Chen, Mingxin Yang, Peng Chen, Peng He, Qingxiang Lin, Runzhou Wu, Sheng Zhang, Shida Wei, Shilin Chen, Shirui Huang, Shuhui Yang, Shu Liu, Sicong Liu, Team Hunyuan3D, Tian Liu, Xianghui Yang, Xiang Yuan, Xiaofeng Yang, Xin Huang, Yifei Feng, Yifu Sun, Yiwen Jia, Yixuan Tang, Yonghao Tan, Yuhong Liu, Yulin Cai, Yunfei Zhao, Zebin He, Zeqiang Lai, Zheng Ye, Zibo Zhao

Hunyuan3D 2.1 generates high-fidelity 3D assets with production-ready PBR materials from images using two dedicated models.

arxiv:2506.15442 v1 · 2025-06-18 · cs.CV · cs.AI

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Record completeness

1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
✓ Portable graph bundle live · download bundle · merged state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The system comprises two core components: the Hunyuan3D-DiT for shape generation and the Hunyuan3D-Paint for texture synthesis.

C2weakest assumption

That the outlined data preparation, architecture, and training strategies will reliably produce high-resolution, production-ready 3D assets with PBR materials.

C3one line summary

Hunyuan3D 2.1 is a two-part system with DiT for shape generation and Paint for texture synthesis that produces high-fidelity 3D assets with PBR materials.

References

47 extracted · 47 resolved · 13 Pith anchors

[1] Denoising diffusion probabilistic models 2020
[2] High-resolution image synthesis with latent diffusion models 2022
[3] Scaling rectified flow transformers for high-resolution image synthesis 2024
[4] Hunyuan-dit: A powerful multi-resolution diffusion transformer with fine-grained chinese understanding 2024
[5] Hunyuanvideo: A systematic framework for large video generative models, 2024 2024

Formal links

2 machine-checked theorem links

Cited by

16 papers in Pith

Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details
UniRecGen: Unifying Multi-View 3D Reconstruction and Generation
3D-ReGen: A Unified 3D Geometry Regeneration Framework
On the Generation and Mitigation of Harmful Geometry in Image-to-3D Models
Pixal3D: Pixel-Aligned 3D Generation from Images
Receipt and verification
First computed 2026-05-17T23:38:12.681366Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

91ef071d5ceda31390b00817ab196441f1605794a800baf18e056f380428ac22

Aliases

arxiv: 2506.15442 · arxiv_version: 2506.15442v1 · doi: 10.48550/arxiv.2506.15442 · pith_short_12: SHXQOHK45WRR · pith_short_16: SHXQOHK45WRRHEFQ · pith_short_8: SHXQOHK4
Agent API
Resolver JSON Graph JSON Events JSON Schema Signing key
Verify this Pith Number yourself 
curl -sH 'Accept: application/ld+json' https://pith.science/pith/SHXQOHK45WRRHEFQBAL2WGLEIH \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 91ef071d5ceda31390b00817ab196441f1605794a800baf18e056f380428ac22
Canonical record JSON 
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    "abstract_canon_sha256": "72ca3e884bbe9dbd80bdc5b403861dca961ba057c2d722286eddc3bb2514e3ca",
    "cross_cats_sorted": [
      "cs.AI"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-06-18T13:14:46Z",
    "title_canon_sha256": "f35f62972eb72abc4019386546203a83a755e5d6f95954d32d7f0b29571c6c4d"
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    "kind": "arxiv",
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