arxiv: 2604.11006 · v1 · submitted 2026-04-13 · 💻 cs.CV

Recognition: unknown

Towards Realistic 3D Emission Materials: Dataset, Baseline, and Evaluation for Emission Texture Generation

Zhiyuan Zhang , Zijian Zhou , Linjun Li , Long Chen , Hao Tang , Yichen Gong

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:08 UTC · model grok-4.3

classification 💻 cs.CV

keywords emission texture generation3D texture synthesisemission materials3D mesh texturingObjaverse-Emission datasetglowing effectscomputer visionPBR materials

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The pith

A new task and 40k-asset dataset let 3D meshes generate emission textures that reproduce glowing effects from reference images.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing 3D texture methods produce only non-emissive materials such as albedo or roughness maps, so they cannot create popular glowing styles like cyberpunk LEDs. The paper defines emission texture generation as the task of synthesizing textures that make 3D objects match the emission appearance shown in an input reference image. To support the task it releases the Objaverse-Emission collection of 40,000 3D assets carrying high-quality emission materials. It also supplies a baseline model called EmissionGen and a set of evaluation metrics that measure how faithfully the output textures reproduce the reference emission effects. If successful, the approach would let synthesized objects display realistic self-illumination without hand-crafted maps.

Core claim

By releasing the Objaverse-Emission dataset of 40k 3D assets with emission materials, introducing the EmissionGen baseline, and defining dedicated evaluation metrics, the work shows that emission texture generation is feasible and enables 3D objects to faithfully reproduce glowing materials from reference images.

What carries the argument

EmissionGen baseline, a model trained on the Objaverse-Emission dataset to produce emission texture maps for untextured 3D meshes given a reference image.

If this is right

3D objects can now carry self-illuminated materials such as LEDs or neon without manual authoring of emission maps.
Texture generation pipelines can be extended beyond standard PBR channels to include emission as a first-class output.
Applications in games and virtual production gain access to reference-driven glowing styles that were previously unavailable.
The same dataset and metrics can serve as a benchmark for any future method addressing emission texture synthesis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Combining the emission task with existing albedo and roughness generators could produce complete material stacks from a single reference.
If the dataset includes varied lighting conditions, models might learn emission behavior that remains consistent when the object is placed in new scenes.
Human preference studies could be used to refine the proposed metrics so they better predict perceived realism of the glow.

Load-bearing premise

That a model trained on the new dataset will generate emission textures whose visual appearance matches the reference images when rendered under typical lighting.

What would settle it

A side-by-side rendering test on held-out objects where the generated emission maps produce visibly different glow intensity or color compared with the reference images.

Figures

Figures reproduced from arXiv: 2604.11006 by Hao Tang, Linjun Li, Long Chen, Yichen Gong, Zhiyuan Zhang, Zijian Zhou.

**Figure 2.** Figure 2: Overview of the Objaverse-Emission dataset construction pipeline. Starting from Objaverse 1.0 and Objaverse-XL, we [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Dataset statistics of Objaverse-Emission: (a) word [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Model architecture of EmissionGen. Following Hunyuan3D-2.1 Paint, we use a dual-branch architecture. The input to [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 6.** Figure 6: Multi-view renderings of a 3D asset under varying [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

**Figure 5.** Figure 5: Glare disturbance from emission materials. The [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 7.** Figure 7: Qualitative comparison of different models. The left most column are reference images. On the right are multi-view [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: With ESDT (Emission Strength Disentanglement [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗

read the original abstract

3D texture generation is receiving increasing attention, as it enables the creation of realistic and aesthetic texture materials for untextured 3D meshes. However, existing 3D texture generation methods are limited to producing only a few types of non-emissive PBR materials (e.g., albedo, metallic maps and roughness maps), making them difficult to replicate highly popular styles, such as cyberpunk, failing to achieve effects like realistic LED emissions. To address this limitation, we propose a novel task, emission texture generation, which enables the synthesized 3D objects to faithfully reproduce the emission materials from input reference images. Our key contributions include: first, We construct the Objaverse-Emission dataset, the first dataset that contains 40k 3D assets with high-quality emission materials. Second, we propose EmissionGen, a novel baseline for the emission texture generation task. Third, we define detailed evaluation metrics for the emission texture generation task. Our results demonstrate significant potential for future industrial applications. Dataset will be available at https://github.com/yx345kw/EmissionGen.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper carves out emission texture generation as a new task with the first dedicated 40k-asset dataset and a baseline, addressing a real gap in PBR-only 3D texture work.

read the letter

The main takeaway is that standard 3D texture methods stop at non-emissive PBR maps and cannot handle glowing effects like LEDs or cyberpunk lighting. This work defines emission texture generation to fix that, releases Objaverse-Emission as the first dataset with 40k assets carrying emission materials, proposes EmissionGen as a baseline, and supplies matching evaluation metrics. The dataset release is the clearest value here because it gives the community concrete data to train on instead of having to scrape or synthesize emission examples from scratch. The baseline shows the task is at least feasible to set up, and the metrics give a starting point for measuring how well generated textures reproduce reference emission. That combination opens a narrow but practical sub-area that prior papers simply ignored. The soft spot is the lack of any numbers in the abstract and the modest claim of “significant potential” without clear benchmarks against adapted existing methods. If the full experiments only demonstrate basic functionality rather than clear improvements in fidelity or efficiency, the method contribution stays secondary to the data. The paper is aimed at graphics researchers who already work on texture synthesis or 3D asset pipelines and want to extend them to emissive cases. It is worth sending to peer review because the task definition and dataset are new and usable even if the baseline needs more comparison work in revision.

Referee Report

0 major / 3 minor

Summary. The paper introduces a novel task of emission texture generation to enable 3D objects to reproduce emissive materials from reference images, extending beyond standard non-emissive PBR textures. Key contributions include the Objaverse-Emission dataset with 40k 3D assets featuring high-quality emission materials, the EmissionGen baseline method, and a set of task-specific evaluation metrics. The authors present experimental results demonstrating feasibility and potential for industrial applications such as cyberpunk-style rendering.

Significance. If the dataset proves high-quality and the baseline demonstrates reliable performance, this work addresses a genuine gap in 3D texture synthesis by supporting emissive effects that current methods cannot handle. Releasing a large-scale dataset is a concrete community contribution that can enable follow-on research, while the baseline and metrics provide a starting point for evaluating emission fidelity. This has clear potential value for graphics applications in gaming, VR, and design.

minor comments (3)

Abstract: the claim that results 'demonstrate significant potential for future industrial applications' would be strengthened by including at least one key quantitative result (e.g., a metric score or comparison) rather than leaving the abstract entirely qualitative.
Dataset section: additional details on the filtering criteria, emission map extraction process, and quality verification steps for the 40k assets would improve reproducibility and allow readers to assess potential biases in the data distribution.
Evaluation metrics: the paper should explicitly justify why the chosen metrics are appropriate for emission effects (e.g., via correlation with human perception studies or comparison to standard image metrics) rather than simply defining them.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, recognition of the significance of the emission texture generation task, and recommendation for minor revision. We are glad the work is seen as addressing a genuine gap with a concrete dataset contribution.

Circularity Check

0 steps flagged

No significant circularity: new task, dataset, and baseline are self-contained contributions

full rationale

The paper defines a new task (emission texture generation), releases the Objaverse-Emission dataset of 40k assets, introduces the EmissionGen baseline architecture, and specifies task-specific metrics. No mathematical derivation chain, fitted parameters, or equations are present that could reduce outputs to inputs by construction. The central claims rest on direct construction of data and a baseline method rather than any self-referential prediction or uniqueness theorem imported via citation. This matches the expected honest non-finding for dataset-plus-baseline papers in computer vision.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no concrete free parameters, axioms, or invented entities can be extracted. The work appears to rest on standard machine-learning assumptions for texture synthesis and dataset curation rather than novel postulates.

pith-pipeline@v0.9.0 · 5503 in / 1071 out tokens · 66963 ms · 2026-05-10T15:08:51.355020+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

33 extracted references · 11 canonical work pages · 4 internal anchors

[1]

Brent Burley and Walt Disney Animation Studios. 2012. Physically-based shading at disney. InAcm siggraph, Vol. 2012. vol. 2012, 1–7

2012
[2]

Siyu Cao, Hangting Chen, Peng Chen, Yiji Cheng, Yutao Cui, Xinchi Deng, Ying Dong, Kipper Gong, Tianpeng Gu, Xiusen Gu, et al. 2025. Hunyuanimage 3.0 technical report.arXiv preprint arXiv:2509.23951(2025)

work page arXiv 2025
[3]

Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qix- ing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. 2015. Shapenet: An information-rich 3d model repository.arXiv preprint arXiv:1512.03012(2015)

work page internal anchor Pith review arXiv 2015
[4]

Dave Zhenyu Chen, Yawar Siddiqui, Hsin-Ying Lee, Sergey Tulyakov, and Matthias Nießner. 2023. Text2tex: Text-driven texture synthesis via diffusion models. InProceedings of the IEEE/CVF international conference on computer vision. 18558–18568

2023
[5]

Wei Cheng, Juncheng Mu, Xianfang Zeng, Xin Chen, Anqi Pang, Chi Zhang, Zhibin Wang, Bin Fu, Gang Yu, Ziwei Liu, et al. 2025. Mvpaint: Synchronized multi-view diffusion for painting anything 3d. InProceedings of the Computer Vision and Pattern Recognition Conference. 585–594

2025
[6]

Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al
[7]

Objaverse-xl: A universe of 10m+ 3d objects.Advances in Neural Information Processing Systems36 (2023), 35799–35813

2023
[8]

Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli Van- derBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi
[9]

InProceedings of the IEEE/CVF conference on computer vision and pattern recognition

Objaverse: A universe of annotated 3d objects. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 13142–13153
[10]

Yifei Feng, Mingxin Yang, Shuhui Yang, Sheng Zhang, Jiaao Yu, Zibo Zhao, Yuhong Liu, Jie Jiang, and Chunchao Guo. 2025. Romantex: Decoupling 3d- aware rotary positional embedded multi-attention network for texture synthesis. InProceedings of the IEEE/CVF International Conference on Computer Vision. 17203–17213

2025
[11]

Dong Guo, Faming Wu, Feida Zhu, Fuxing Leng, Guang Shi, Haobin Chen, Haoqi Fan, Jian Wang, Jianyu Jiang, Jiawei Wang, et al . 2025. Seed1. 5-vl technical report.arXiv preprint arXiv:2505.07062(2025)

work page internal anchor Pith review arXiv 2025
[12]

Zebin He, Mingxin Yang, Shuhui Yang, Yixuan Tang, Tao Wang, Kaihao Zhang, Guanying Chen, Yuhong Liu, Jie Jiang, Chunchao Guo, et al. 2025. MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion.arXiv preprint arXiv:2503.10289(2025)

work page arXiv 2025
[13]

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems30 (2017)

2017
[14]

Team Hunyuan3D, Shuhui Yang, Mingxin Yang, Yifei Feng, Xin Huang, Sheng Zhang, Zebin He, Di Luo, Haolin Liu, Yunfei Zhao, et al. 2025. Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material. arXiv preprint arXiv:2506.15442(2025)

work page arXiv 2025
[15]

Weiyu Li, Xuanyang Zhang, Zheng Sun, Di Qi, Hao Li, Wei Cheng, Weiwei Cai, Shihao Wu, Jiarui Liu, Zihao Wang, et al. 2025. Step1x-3d: Towards high-fidelity and controllable generation of textured 3d assets.arXiv preprint arXiv:2505.07747 (2025)

work page arXiv 2025
[16]

Chendi Lin, Heshan Liu, Qunshu Lin, Zachary Bright, Shitao Tang, Yihui He, Minghao Liu, Ling Zhu, and Cindy Le. 2025. Objaverse++: Curated 3D Object Dataset with Quality Annotations.arXiv preprint arXiv:2504.07334(2025)

work page arXiv 2025
[17]

Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka. 2022. Text2mesh: Text-driven neural stylization for meshes. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 13492–13502

2022
[18]

Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa
[19]

InSIGGRAPH Asia 2022 conference papers

Clip-mesh: Generating textured meshes from text using pretrained image- text models. InSIGGRAPH Asia 2022 conference papers. 1–8

2022
[20]

Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El- Nouby, et al. 2023. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193(2023)

work page internal anchor Pith review Pith/arXiv arXiv 2023
[21]

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sand- hini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al
[22]

In International conference on machine learning

Learning transferable visual models from natural language supervision. In International conference on machine learning. PmLR, 8748–8763
[23]

Mingqi Shao, Feng Xiong, Zhaoxu Sun, and Mu Xu. 2025. MVPainter: Accurate and Detailed 3D Texture Generation via Multi-View Diffusion with Geometric Control.arXiv preprint arXiv:2505.12635(2025)

work page arXiv 2025
[24]

Carole H Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, and M Jorge Car- doso. 2017. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. InInternational Workshop on Deep Learning in Medical Image Analysis. Springer, 240–248

2017
[25]

Tencent Hunyuan Team. 2025. Hunyuan 3D: A High-Resolution Text-to-3D and Image-to-3D Generation Model. https://3d.hunyuan.tencent.com/. Accessed on: 2025-11-14

2025
[26]

Volcengine. 2024. Doubao Large Model. https://www.volcengine.com/product/ doubao. Accessed on 2025-11-14

2024
[27]

Haoyuan Wang, Zhenwei Wang, Xiaoxiao Long, Cheng Lin, Gerhard Hancke, and Rynson WH Lau. 2025. MAGE: Single Image to Material-Aware 3D via the Multi-View G-Buffer Estimation Model. InProceedings of the Computer Vision and Pattern Recognition Conference. 10985–10995

2025
[28]

Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing13, 4 (2004), 600–612

2004
[29]

Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng- ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al . 2025. Qwen-image technical report.arXiv preprint arXiv:2508.02324(2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025
[30]

Xin Yu, Ze Yuan, Yuan-Chen Guo, Ying-Tian Liu, Jianhui Liu, Yangguang Li, Yan-Pei Cao, Ding Liang, and Xiaojuan Qi. 2024. Texgen: a generative diffusion model for mesh textures.ACM Transactions on Graphics (TOG)43, 6 (2024), 1–14

2024
[31]

Christina Zhang, Simran Motwani, Matthew Yu, Ji Hou, Felix Juefei-Xu, Sam Tsai, Peter Vajda, Zijian He, and Jialiang Wang. 2024. Pixel-space post-training of latent diffusion models.arXiv preprint arXiv:2409.17565(2024)

work page arXiv 2024
[32]

Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang
[33]

InProceedings of the IEEE conference on computer vision and pattern recognition

The unreasonable effectiveness of deep features as a perceptual metric. InProceedings of the IEEE conference on computer vision and pattern recognition. 586–595. 9