Pith Number

pith:LE7WGICD

pith:2026:LE7WGICDUR7IKHGMZFIUYT6W5E

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DealMaTe: Multi-Dimensional Material Transfer via Diffusion Transformer

Jie Guo, Nisha Huang, Tong-Yee Lee, Xiu Li, Yizhou Lin, Zitong Yu

DealMaTe transfers materials across objects using depth, normal, and lighting images in a text-free diffusion framework.

arxiv:2605.15681 v1 · 2026-05-15 · cs.GR · cs.CV

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Claims

C1strongest claim

Extensive experiments covering a wide variety of objects and lighting conditions consistently demonstrate that DealMaTe achieves remarkable high-fidelity material transfer under arbitrary input materials.

C2weakest assumption

The lightweight 3D information injection method (Multi-Dim 3D Shader LoRA) enables compatible control conditions and achieves harmonious and stable results without modifying the base model weights.

C3one line summary

DealMaTe proposes a simplified diffusion framework for material transfer that injects multi-dimensional 3D conditions via Multi-Dim 3D Shader LoRA and Shader Causal Mutual Attention with KV caching.

References

71 extracted · 71 resolved · 2 Pith anchors

[1] Louis-Philippe Asselin, Denis Laurendeau, and Jean-Francois Lalonde. 2020. Deep SVBRDF estimation on real materials. InInternational Conference on 3D Vision (3DV). IEEE, 1157–1166 2020

[2] Tim Brooks, Aleksander Holynski, and Alexei A Efros. 2023. Instructpix2pix: Learning to follow image editing instructions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog 2023

[3] George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. 2022. Wearable ImageNet: Synthesizing tileable textures via dataset distillation. InProceedings of the IEEE/CVF Co 2022

[4] Dave Zhenyu Chen, Yawar Siddiqui, Hsin-Ying Lee, Sergey Tulyakov, and Matthias Nießner. 2023. Text2tex: Text-driven texture synthesis via diffusion models. In Proceedings of the IEEE/CVF International 2023

[5] Junsong Chen, YU Jincheng, GE Chongjian, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. 2024. Pixart-𝛼: Fast training of diffusion transformer for photorealistic 2024

Receipt and verification

First computed	2026-05-20T00:01:12.089569Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

593f632043a47e851cccc9514c4fd6e930aa571d9f6525961cbb80247fcf51e2

Aliases

arxiv: 2605.15681 · arxiv_version: 2605.15681v1 · doi: 10.48550/arxiv.2605.15681 · pith_short_12: LE7WGICDUR7I · pith_short_16: LE7WGICDUR7IKHGM · pith_short_8: LE7WGICD

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LE7WGICDUR7IKHGMZFIUYT6W5E \
  | 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: 593f632043a47e851cccc9514c4fd6e930aa571d9f6525961cbb80247fcf51e2

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.GR",
    "submitted_at": "2026-05-15T07:06:39Z",
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