LumiTex: Towards High-Fidelity PBR Texture Generation with Illumination Context
Authors: Jingzhi Bao
HONGZE CHEN
Lingting Zhu
Chenyu Liu
Runze Zhang
keyang luo
Zeyu HU
Weikai Chen
Yingda Yin
Xin Wang
Zehong Lin
Jun Zhang
Xiaoguang Han
Venue: ICLR 2026 Poster (2026)
Keywords: Texture Generation
3D Generation
Diffusion Models
Physically Based Rendering
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Physically-based rendering (PBR) provides a principled standard for realistic material–lighting interactions in computer graphics. Despite recent advances in generating PBR textures, existing methods fail to address two fundamental challenges: 1) materials decomposition from image prompts under limited illumination cues, and 2) seamless and view-consistent texture completion. To this end, we propose LumiTex, an end-to-end framework that comprises three key components: (1) a multi-branch generation scheme that disentangles albedo and metallic–roughness under shared illumination priors for robust material understanding, (2) a lighting-aware material attention mechanism that injects illumination context into the decoding process for physically grounded generation of albedo, metallic, and roughness maps, and (3) a geometry-guided inpainting module based on a large view synthesis model that enriches texture coverage and ensures seamless, view-consistent UV completion. Extensive experiments demonstrate that LumiTex achieves state-of-the-art performance in texture quality, surpassing both existing open-source and commercial methods. Project page: [https://lumitex.vercel.app](https://lumitex.vercel.app).

Published as a conference paper at ICLR 2026
LUMITEX: TOWARDS HIGH-FIDELITY PBR TEXTURE GENERATION WITH ILLUMINATION CONTEXT
Jingzhi Bao' Hongze Chen²; Lingting Zhu², Chenyu Liu', Keyang Luo², Runze Zhang Zeyu Hu", Yingda Yin Weikai Cheno, Xin Wang", Zehong Lin Jun Zhang', Xiaoguang Han Han 1.78t
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen
The Hong Kong University of Science and Technology
'The University of Hong Kong, "Peking University, SLIGHTSPEDD, *Lingnan University
Shenzhen Future Network of Intelligence Institute
Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong. Shenzhen zehongl nl In.udu. hk, hanxiaoguang@ www.:eeecccckkc..cccoooo edu.cn
Figure 1. collection of textured meshes with PBR materials generated by LumiTex, capturing both intricate details and convincing physical realism across diverse object categories.
ABSTRACT
Physically-based rendering (PBR) provides a principled standard for realistic ma- terial-lighting interactions in computer graphics. Despite recent advances in gen- erating PBR textures, existing methods fail to address two fundamental chal- lenges: 1) materials decomposition from image prompts under limited illumi- nation cues, and 2) seamless and view-consistent texture completion. To this end, propose LumiTex, end-to-end framework that comprises three key components: (1) a multi-branch generation scheme that disentangles albedo and metallic-roughness under shared illumination priors for robust material under- standing, (2) a lighting-aware material attention mechanism that injects illumi- nation context into the decoding process for physically grounded generation of albedo, metallic, and roughness maps, and (3) a geometry-guidee module based on a large view synthesis model that enriches texture coverage and ensures seamless, view-consistent UV completion. Extensive experiments demonstrate that LumiTex achieves state-of-the-an performance in texture qual- ity, surpassing both existing open-source and commercial methods. Project page: http://fumiiex..veeel.aap
inpainting
Equual contribution. Corresponding authors.

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Published as a conference paper at ICLR 2026
1 INTRODUCTION
Physically-bass -aased rendering (PBR) is the industry standard for material and lighting representation in games, films, and AR/VR. PBR textures encode key surface properties such as albedo and metal- lic secrrousnesss (MR), allowing for realistic visual interactions under diverse lighting conditions. Nev- ertheless, it is challenging to generate PBR textures, which requires both accurate physical charac- terization of materials and consistency across multiple viewpoints.
To obtain the high-quality texture from a mesh and image, multi-view texturing has emerged as the dominant framework across both research (Huang et al., 2024c; Liang et al., 2025b; Zhang et al., 2024a; Zhao et al., 2025) and commercial systems (TripoAI, 2025; Mcshy, 2025: Team, 2025b). At the core of this framework are multi-view diffusion models, which jointly reason about the underly- ing 3D structure and consistent appearance across all views through multi-view attention (Shi et al., 2023). This mechanism links pixels across viewpoints to ensure coherent geometry and appearance, after which a back-projection step aggregates the synthesized views into UV texture maps. A key strength of this paradigm is that inherits the visual quality and diversity of pre-trained image dif- fusion models. Consequently, adapting these models to handle PBR-aware multi-view generation provides a unified pipeline that naturally bridges reference images with textured 3D assets.
Recent advances in PBR texture generation, primarily driven by the remarkable capabilities of multi-view diffusion models, can be broadly categorized into two main approaches. The first line of research (Zhang et al., 2024c; Zhu et al., 2025; Hong et al., 2024; Munkberg et al., 2025) adopts a two-stage approach: it first generates multi-view images that encode baked environment lighting (shaded images), and then obtains PBR textures either through optimization or by employing dedi- cated multi-view inference models, e.g., IDArb (Li et al., 2025b). These shaded images supply rich illumination cues that crucial for accurate material decomposition. However, optimization-bart -based methods such as DreamMat (Zhang et al., 2024c) are severely limited by long optimization time. While multi-view inference models can help in efficiency, existing methods like MuMA (Zhu al., 2025) often produce inferior materials, as the generated intermediates are frequently suboptimal and poorly aligned with the training inputs, as illustrated in Fig. 2(a).
The second stream (Zhao et al., 2025; He et al., 2025: Zhu al., 2024; Huang et al., 2025) focuses on jointly generating multi-view albedo, metal- lic, and roughness images with multi- channel diffusion (see Fig. 2(b)) and subsequently baking multi-view im- ages to the UV space via camera pro- jection (Chen et al., 2023b; Richard- son et al., 2023). However, these methods suffer from three key limi- tations. Firstly, generating accurate multi-view material images is chal- lenging since image diffusion mod- els (Ho al., 2020; Podell et al., 2023; Lin et al., 2024; Esser et al., 2024) lack sufficient material priors, and reference images provide only limited illumination cues. Secondly, the domain gap between albedo and metallic-roughnes (MR) is often overlooked. Albedo reflects the intrinsic surface color, whereas MR encodes illumination- dependent material properties. However, multi-channel approaches typically predict them jointly within shared output space. This entangled representation, without explicit contextual guidance, hinders accurate material decomposition and compromises the physical plausibility of the gener- ated textures. Lastly, existing datasets suffer from severe data imbalance. While shaded and albedo images are relatively abundant, high-quality metallic and roughness maps are much more scarce, limiting effective supervision for PBR materials.
(o) fooesaage Gemerriin
Goneration
PBR Decomposiion
Shoded
Loyer
Albico
Mull-Chiammad Geratttinn
(c) Clur
M. di -eennchh fleckk
Shoaded Block
UNt
Crocect
Waterial Block N
Figure 2. Illustration of Different PBR Modeling. Un- like (a) two-stage PBR texture generation with subopti- mal intermediates, and (b) end-to-end approach w/o multi- view shaded features, (c) our multi-branch design lever- ages multi-view consistent lighting features and surpasses the generation quality of other models.
To address these limitations, we propose LumiTex, an end-to-end framework that jointly generates multi-view shaded images and PBR maps. Our key idea is integrate multi-stage inference into
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single one overcome the reliance imperfect shaded intermediates while alleviating the data imbalance and retaining rich illumination cues via multi-stage training. Specifically, we first train multi-view illumination context branch to reconstruct shaded images across views, capturing rich and consistent lighting cues as an explicit illumination context. To address the domain gap between albedo and MR in joint modeling, we then introduce a lighting-aware material attention mecha- nism in the material branches that separately guide albedo and MR by the illumination context for channel-specific reasoning. By combining the generative prior of diffusion models with the rich illumination context extended from reference images, LumiTex enables high-fidelity and physically plausible texture generation in a fully end-to-end manner.
To further enhance the quality and completeness of generated textures, we introduce a general texture inpainting strategy based on large view synthesis model (LVSM) (Jin et al., 2025). Specifically, we train geometry-guidee LVSM to synthesize novel views for missing or occluded regions based on the generated views. Given input views, geometry maps, and camera poses, the model synthe- sizes new viewpoints from arbitrary target poses, effectively densifying the texture observations. In contrast to UV-based methods (Yu et al., 2023; Cheng et al., 2024; Yu et al., 2024; Zeng et al., 2024a), which suffer from discontinuities and topological ambiguities in UV space, approach performs view-space completion, enabling seamless and globally consistent texture generation.
The contributions of this work are summarized as follows:
We propose an end-to-end multi-branch framework for high-quality PBR texture generation, where a multi-view illumination context branch captures consistent lighting priors to alleviate data imbalance and reliance imperfect intermediates in two-stage designs.
We introduce lighting-aware material attention mechanism that conditions albedo and metal- lic-roughness generation on shared illumination priors, enabling disentangled material reasoning and improving physical plausibility.
We propose an advanced texture inpainting strategy, leveraging LVSM to extend the generated views to denser set for seamless and globally consistent texture completion.
Extensive experiments demonstrate that our method surpasses existing state-of-the-art open- source and commercial methods.
2 RELATED WORK
Texture Generation. The advances of foundation models have opened new directions for automat- ing texture generation in 3D content creation. Early works leverage 2D diffusion priors via Score Distillation Sampling (SDS) to optimize 3D) assets (Poole et al., 2022; Lin et al., 2023; Wang et al., 2023; Po & Wetzstein, 2024; Metzer et al., 2022: Chen et al., 2023a; Khalid et al., 2022; Michel et al., 2021; Chen et al., 2022). These approaches iteratively optimize renderings of 3D shapes to align with the distribution learned by pre-trained diffusion models (Podell et al., 2023; Rombach et al., 2021; Esser et al., 2024; Lin et al., 2024), but the results are often over-saturated and unreg- ulated for 3D shapes, making them inapplicable for practical use. To enhance geometric fidelity, some methods (Yu al., 2023; Bensadoun al., 2024; Cao al., 2023; Liu et al., 2024a) incorpo- rate explicit 3D features, such as vertex positions, normals, or depths, to progressively inpaint the mesh across pre-defined views. Although the geometry fidelity is improved, the results are deterio- rated by the synchronization process of multi-view latents. Another line of research (Yu et al., 2023; Zeng et al., 2024a; Bensadoun et al., 2024; Yu et al., 2024) projects the 3D point cloud information and supervises the training in the UV space, addressing the occlusion problem in the multi-view approaches. However, they introduce topological ambiguity inherent from the UV representation, deteriorating the capability of the diffusion model to generate high-fidelity textures.
More recently, methods like MV-Adapter (Huang et al., 2024c) and Hunyuan3D- 2025) have shown promising results in generating globally consistent textures via multi-view atten- tion (Li et al., 2024; Kant et al., 2024; Huang et al., 2024d: Shi et al., 2023; Wang & Shi, 2023; Feng et al., 2025). These methods efficiently leverage the capability of pre-trained diffusion models and the 3D geometry condition, ensuring both realistic results and spatial consistency.
(Zhao al.,
PBR Texture Generation. Recent approaches leverage pre-trained diffusion models to generate PBR materials for 3D assets. Early works employ SDS for PBR generation and typically (Chen et al., 2023e; Zhang et al., 2024e; Liu et al., 2024b; Youwang et al., 2024a; Wu et al., 2023; Xu et al., 2023;
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In Mesh
Referance
DINOv2
Shaded Images
Mulii-Vien
Material Branch
VAE
texture tepoiting
Geomotty Condition
Mutti-Modal DIT
Mulli-View DIT
Albedo
Mulli-Modal Blocks
Multi-view Blocks
Transformer A/
RoPE
View oware RoFE
Tokcn Droscutl
K
Copy
Capyy
K
Figure 3. Overview of LumiTex. We first train multi-view illuiination-oonistee base model to generate shaded images. Then, we freeze this branch and utilize its shaded features to train material branches for figh-iidlity PBR texture generation. Finally, geometry-guidee LVSM synthesizes novel views from novel perspectives to enable seamless, view-consistent texture inpainting.
Yeh et al., 2024) incorporate the BRDF in the diffusion process to learn material properties. Methods like Material-Anythinn (Huang et al., 2024a) and CLAY (Zhang al., 2024a) iteratively denoise and synchronize latents across multiple viewpoints. TexGaussian (Xiong et al., 2024) leverages octant- aligned 3DGS (Kerbl al., 2023) for real-time PBR texture synthesis. Other approaches (Zhang et al., 2024b; Dang et al., 2024: Wang al., 2024: Fang al., 2024) leverage LLMs improve semantic aligmment with surieval-augmente generation. However, these methods struggle with physically grounded decomposition, view consistency, accurate separation of albedo and MR. Some approaches (He et al., 2025; Zhu et al., 2024; Vainer et al., 2024; Vecchio et al., 2024; Sartor & Peers, 2023) fine-tune image models generate multi-channel PBR materials, but the domain gap between albedo and MR is not well addressed.
Image Intrinsic Decomposition. Material decomposition aims estimate intrinsic materials from image inputs with unknown lighting, serving as fundamental task in material understanding. Early works (Wimbauer al., 2022: Sang & Chandraker, 2020: Boss al., 2020; Yi al., 2023) formu- late this task as an optimization problem, recovering the PBR materials from the images by solving the rendering equation (Kajiya, 1986). Recently, generative approaches (Chen al., 2024; Hong et al., 2024; Kocsis et al., 2024; Li et al., 2025b; Zhu et al., 2022; Huang et al., 2024b) utilize dif- fusion models to decompose materials from images with promising results. RGB-X (Zeng et al., 2024b) and DiffusionnRnderer (Liang et al., 2025a) propose unified forward and inverse process for image synthesis and material decomposition. However, estimating individual properties from a single image with unknown illumination is ill- ill-posed due to the inherent ambiguity between illumi- nation and materials. Our work designs a multi-view pipeline that incorporates illumination context, enabling physically plausible and coherent material generation.
METHOD
Given an input mesh with reference image I, our goal is to generate N view-consistent PBR images and achieve seamless material textures. We describe the overall pipeline architecture Sec- tion 3.1. As shown in Fig. 3, we first train a multi-view illumination context branch that reconstructs multi-view shaded images to capture consistent lighting cues as an explicit illumination context. material attention
This context then guides the albedo and MR branches through lighting- mechanism to generate multi-view material maps (Section 3.2). Finally, a scometry-guidee LVSM trained to synthesize M novel views from generated N views for texture inpainting (Section 3.3).
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3.1 MULTI-VIEW PBR GENERATION TRANSFORMER
Our model integrates a multi-modal Transformer (MM-T) and multi-view Transformer (MV-T). The multi-modal transformer fuses diverse modalities for each view, while the multi-view trans- former enforces consistency across views. The detailed operations presented below.
Multi-Modal DIT. The MM-T is designed to integrate geometry information, reference appearance, and material semanties (albedo or MR) for each view. Specifically, for each view = 1,.... NN, we concatenate tokens derived from the input image encoded by VAE and DINOv2 (Oquab et al., 2023), the mesh geometry (normal N, and canonical coordinate C,) encoded by VAE, and learnable material embeddings e, as well as view latent 2;, and feed them into a stack of I transformer blocks:
T geo Lineargen geo
VAB(N;) @ VAB(C.)] E ERNXIXC
T img =
Linearing
VAE(I); DINO(() E R 1X2XXC
(2)
MM-T (zi, Ting T geo: e) -R-NLLCC.
(3)
Multi- DēT. After fusing multi-modal features the first stage, we discard the image and domain tokens, and shift focus enforcing cross-view consistency in the second stage. Specifically, we concatenate their latent tokens from N views into a unified sequence that is processed by sequence of transformer blocks, allowing each token interact with others for global consistency:
( (
2 NLXC,
where 2; the denoised latent for the i-th view, L is the token length, and C is the feature dimension. The whole model is trained end-to-end using flow matching loss on multi-view images.
3.2 LIGHTING-AWARY MATERIAL ATTENTION
Multi-view shaded images provide rich illumination cues for high-fidelity PBR reconstruction, serv- ing as reliable prior for nigh-fidlityy PBR reconstruction. Following this insight, we design a multi- branch generative framework, where multi-view illumination context branch provides shaded em- beddings for material reasoning. The lighting-aware material branch then consumes this context to produce physically plausible PBR maps. This framework alleviates the data scarcity problem in PBR texture generation, mitigates the domain gap between albedo and MR maps, and yields more physically consistent material predictions.
Multi- Vieti-View Illumination Context Branch. Unlike prior two-stage works that rely on multi-view shaded images intermediates, introduce a multi-view illumination context branch to learn the shaded embeddings, as illustrated in Fig. 3. Specifically, this branch is trained to reconstruct multi- view shaded images ensure the learned embeddings capture consistent illumination across views. To model view-dependent illumination effects, shaded latent tokens S = {8, (8;); ) from all views are encoded with a view- wware RoPE (Su al., 2024), which preserves both spatial alignment and view identity. Then, we perform cross-view attention to produce shaded keys and values that condition the material branches:
Σ, Softmax, kq:kf + +(t, Uy. K Kaladdd = WK S, Vahaded WyS,
(5)
where @tt, i, is a view-specific positional embedding tied to the index of query view t, encoding illumination relationships between views. We provide the details in Sec. A.2 of the Appendix.
This design alleviates the data imbalance issue in open-source 3D datasets, where high-quality PBR maps are searce. In this way, even data that lack plausible MR maps can utilized, as they still pro- vide supervision for multi-view illumination reconstruction under physically consistent materials.
Lighting-Awan Material Branch. Some works generate PBR materials via jointly modeling the albedo and MR maps, overlooking the domain gap between these two modalities. To address this problem, we leverage the illumination context learned from the previous branch and guide albedo and MR generation separately, while conditioning both the same shaded features to maintain the consistency the two branches. Specifically, we introduce a lighting-awad aware material attention mechanism in which albedo and MR branches perform shaded-guided cross-attentio The shaded
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keys Khuded and values Vataded supply illumination cues to inform material-specife queries:
Qalhedo K
shadted
Altee Softmax
Vihuded:
(6)
Vd
Q Qmr K shaueed Vshuded-
Ar Softmax
(7)
Vd
This design provides a rich illumination context for the decoding process, enabling the model to separate reflectance properties from lighting effects and avoid the error accumulation problem in the two-stage design. By complementarily attending to shaded priors, the albedo branch emphasizes diffuse consistency while the MR branch captures specular characteristics, together improving both the physical plausibility and multi-view coherence of generated PBR textures.
3.3 TEXTURE INPAINTING AS NOVEL VIEW SYNTHESIS
Texture inpainting is post-procsssing step that fills missing or occluded regions to ensure seamless integration with surrounding textures. Previous methods usually perform inpainting on partially textured meshes in the UV space. However, due to inherent UV discontinuities and topological ambiguities, the inpainted regions often fail to align with their surrounding areas, particularly when the UV mapping is highly fragmented, as common for shapes produced by generative models.
Incomplete textures typically result from limited surface coverage in the generated sparse multi-view images. To address this, we aim to synthe- size dense set of views that fully cover the object's surface, enabling seamless texture completion through direct back- paojeprojection. To this end, we train a cooometry-guideee variant of LVSM (Jin et al., 2025), a scalable large view synthesis model known for high-quality novel view genera- tion, to infer additional views that cover previously unobserved regions of the mesh. These synthesized views, combined with the initial ones, form a dense set that is projected back UV space, resulting in seamless and complete texture. The proposed framework is illustrated in Fig. 4.
RGB
Geometry
Camera
Geomelry-LVSM
Novel RGB
Decoder-one only only Transformer
Figure 4. Texture Inpainting with Geometry-guided LVSM (Jin et al., 2025). Our model infers dense novel views from sparse inputs and aligns with the geometry.
Novel View Generation. Given N generated sparse view images {1.) pixel-aligned Plücker ray maps (P.) / that encode camera intrinsics and extrinsics, and geometry conditions {G,) infer additional M target views with conditions {P))),, and {G!) to inpaint the occluded re- gions. To effectively embed conditions, we tokenize and map the inputs into a unified representation with linear layer. Formally.
x; MLP([P,, G., LJ), a = MLP([P) G E Rd
where d is the feature dimension, Ø; represents the set of condition tokens, and of denotes the set of target tokens. Following LVSM (Jin et al., 2025), we employ a decoder-only transformer to infer target views from input tokens that avoid explicit 3D representation to minimize the inductive bias:
{y: W M
Transformer ((x:) ) {
where the output condition tokens y: and target tokens yf are updated from the corresponding inputs. Then, we discard condition tokens and map target tokens to the RGB space with an MI.P, followed by reshaping to form the final predicted images i':
P...., Fy = Reshape (MLP(y)..... .yhr))
(10)
To further improve the quality of the synthesized novel views, we adopt the tist-time training method proposed in Zhang et al. (2025). The geometry-gune LVSM generates two dense sets of albedo

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and MR separately for texture inpainting, where MR is encoded in the ORM convention with the occlusion channel set to a constant value.
View Selection. Inspired by Zhao et al. (2025), we select target views from predefined dense set V = {UI,... Ug J. We first project the generated N views into the UV space to obtain an incomplete texture. Then, we greedily rank the views in V by the area of uncovered UV regions they observe and select the top M as target views.
4 EXPERIMENTS
In this section, we evaluate our method with both open-source and commercial state-of-the-art meth- ods, including shaded texture generation, PBR generation, and texture inpainting methods. We con- duct comprehensive qualitative and quantitative comparisons, and a user study with 3D modelers, to demonstrate that our generated PBR textures align well with human perceptual preferences.
4.1 IMPLEMENTATION DETAILS
We curate a dataset from Objaverse and Objaverse-XL (Deitke et al., 2023b;a), containing 92K objects as our training set. For each 3D object, we sample cameras from 30 views and render the object with 3 environment maps. We also render multi-view albedo, metallic, roughness maps, and HDR images at resolution of 1024 1024. For the model training, we initialize our DiT from FLUXX.1-ee (BlackForestl .abs, 2024), utilizing the flow matching as the training objective. We first train our shaded model on 512 x 512 and then scale up to 768 768. The entire training procedure requires approximately 106 GPU days. We provide datasets and training details in Sec. A.2.
4.2 COMPARISON WITH EXISTING METHODS
Baselines. We compare our method with comprehensive texture synthesis baselines to demonstrate its effectiveness. The baselines include shaded texture synthesis: SyneMVD (Liu al., 2024a), MV-Adapter (Huang et al., 2024c), StepIX-3D (Li et al., 2025a), UniTEX (Liang et al., 2025b); and PBR texture synthesis: Paint-it (Youwang et al., 2024b), DreamMat (Zhang et al., 2024c), and Hunyuan3D-2.1 (Team, 2025a) (MaterialMVP (Huang et al.. 2025) enhanced with purchased dataset). Additionally, we compare LumiTex with proprietary methods such as Meshy-5 (Meshy, 2025) and Tripo Al v2.5 (TripoAI, 2025). We improve the original text-conditioned SyncMVD (Liu al., 2024a) by incorporating the SDXL-base model (Podell al., 2023) (the vital component) and an IP-Adapter (Ye et al., 2023) to align with an image-to-texture task and compare it (referred to as SyneMVD-IPA) with our approach. We generate some image prompts using GPT-4o and obtain meshes from H-n2 (Team, 2025b) and Objaverse (Deitke et al., 2023b). For the texture inpainting, we compare our method with Paint3D (Zeng et al., 2024a) and TexGen (Yu et al., 2024).
Evaluation Metrics. We follow Hunyuan3D (Team, 2025a;b; Zhao et al., 2025) to use FID (Heusel et al., 2017). CLIP-FID, and LPIPS (Zhang et al., 2018) to evaluate texture fidelity. CLIP Maximum- Mean Discrepancy (CMMD) (Jayasumana al., 2024) assesses the diversity of the generated texture details, and CLIP-I (Radford et al., 2021) measures prompt alignment. We further assess relighting quality under novel lighting as indicator of the physical accuracy of the generated PBR materials.
4.3 QUALITATIVE RESULTS
We qualitatively compare LumiTee with recent baselines in texture generation and inpainting. Lu- míTex surpasses baselines in physical realism, lighting decoupling, and prompt fidelity. terms of realism, LumiTex exhibits realistic appearances under novel lighting, while others often copy input highlights as in Fig. 5, failing to produce realistic reflections due to the absence of PBR materials. As shown in Figs. 5 and 6, LumiTTe effectively decouples lighting effects to ensure that models interact correctly with environment--epeedeent illumination. contrast, baselines often produce diffuse maps with baked-in lighting, which deteriorates realism and hinders 3D asset reuse. Com- pared to SyncMVD, StepIX-3D, and DreamMat, which exhibit color shifts, our method produces semantically faithful PBR textures that preserve fine details. LumiTex can also generate high- tigh-quality textures across complex shapes and diverse object categories (see Fig. 1), retaining realistic appear- ances under diverse lighting (see Fig. 8), supporting downstream applications like IP design.
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Inputs
SyncMVD-IPA
MV.Adupter
StepIX-3D
UniTEX
Figure 5. Qualitative Results Texture Generation Methods. Our method generates plausible materials for relighting, avoids baked-in lighting, and is robust under diverse reference illuminations.
Inputs
DatamMat
M
Tripo Al v2.5*
Hunyun3D-22I1
Ours
Figure 6. Qualitative Results on PBR Generation Methods. Our method generates high-fidelity PBR materials, avoids light baking, and achieves competitive PBR maps compared state-of-the art open-source and commercial methods. Each object has: the albedo on the left, the metallic on the top right, and the roughness on the bottom right. denotes the method trained on private datasets.
In Fig. 7, we compare texture inpainting results across baselines. Paint3D completes textures with a UV refinement model but often produces over-smoothed and semantically inconsistent results, as spatially unrelated regions are treated equally in the UV domain. For example, the Beetle's underside and wheels are inpainted without spatial awareness, leading to texture bleeding. TexGen mitigates this issue via 3D spatial encoding, yet still suffers from disrupted semantic coherence and seams, due to the inherent topological ambiguities of UV mapping. Our method, conditioned on 2D views and 3D cameras, avoids the adverse effects caused by UV representation. By unifying condition and target views in a framework, our method preserves fine details while ensuring global consistency.
4.4 QUANTITATIVE RESULTS
We evaluate 133 objects excluded from training on two aspects: texture quality and relighting eval- uation. Texture quality assesses fidelity and alignment with the reference image, while relighting
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Pattial Testured Medi
Reflveace
PlannDD
TexGen
Ours
Patiil Testueed Mehh
Reference
PainSDD
TexGen
Ouss
Figure 7. Comparison with Texture Inpainting Methods. Our approach effectively recovers local details and exhibits greater robustness than other methods, producing semantically coherent results without visible seams.
Table 1. Quantitative comparison with state-of-the-e methods. We compare two classes of meth- ods, texture-only generation and PBR texture generation. Our method achieves the best performance compared with both classes. denotes the method trained on private datasets.
Texture Evaluation
Relighting Evaluation
Method
Typo FID.
CLIP-FIDJ
CMMD.
CLIP-11
LPIPS]
FID
CLIP-FID
CMMD]
CLIP-17
LPIPS]
SyncMVD-IPA (L.u al., 2024d)
Texture 222.1
21.10
1.8263
0.9187
0.2504
149.1
18.04
0.7394
0.9101
0.1202
MV-Adapier (Hlung al., 2024c)
Texture 237.3
24.95
2.4510
0.9022
0.2574
123.2
13.82
0.5405
0.9246
0.1034
Step1X-3DD al., 2025a)
Texture 240.9
24.32
2.2090
0.9053
0.2540
120.0
12.90
0,4038
0,9288
0.1000
UnITEX (Liong ct.,., 2025b)
Texture 230.7
22.20
1.9891
0.9133
0.2473
124.8
13.50
0.4707
0,9282
0.9974
Paint-it (Yoowang al., 2024h)
PBR 293.3
35.50
3.4137
0.8688
0.3769
162.9
26.77
1,2514
0.8666
0.1564
DreamMat (Zhang et al., 2024c)
PBR 231.6
25.49
2.1722
0.9016
0.2816
160.1
19:97
0.8386
0.8983
0.1346
HunyuanSD 2.1 [Team, 2025a)
PBR 196.6
18.84
1.7195
0.9268
0.2413
103.7
10.89
0,3610
0,9420
0,0808
Ours
PBR 160.8
14.89
1.3669
0,9417
0.1903
99.6
10.63
0.3151
0.9436
0.0831
evaluation involves rendering each object from 32 views sampled on the Fibonacci sphere with ran- dom environment maps and comparing them against ground truth renderings. As shown in Tab. 1, our method surpasses both texture-only and PBR-based baselines, achieving better FID and LPIPS for visual fidelity, lower CMMD for richer and more diverse texture generation, and stronger seman- tic alignment with the reference images as reflected by higher CL.PP-I and lower CLIP-FID scores.
To evaluate perceptual quality, we conduct a user study, where 23 3D modelers rate results (ranging from to 5) generated from different meth- ods five criteria that are diffi- cult to quantify: rendering quality, albedo, roughness, metallic accuracy, and texture completeness, with input images and meshes provided for ref- erence. As shown in Tab. 2, our method outperforms all baselines across all criteria, demonstrating strong alignment with human preference.
Table 2. User study results rendering quality, complete- ness, and PBR matcrial accuracy.
Method
Quality? Compl. Diffuse" Metallic Rough.1
SyncMVD (Lio al. 2024a)
2.29
3.27
2.39
MV-Adapter (Huang et al., 2024c)
2.65
3.06
2.65
Step 3D (li al., 2025a)
2.70
2.98
2.58
UniTEX (Liung et al., 2025b)
2.96
2.98
2.75
Paint it (Youwang
al., 2024b)
2.27
2.97
2.19
2.46
2.57
DreamMat (Zhang et all,, 2024c)
2.05
2.92
2.58
2.40
2.33
Honyuan3D-2.1 (Team, 2025a)
3.69
3.98
3.57
3.34
3.61
Ours
4.48
4.61
4.34
4.14
4.07
4.5 ABLATION STUDY
One-Stage Generation. To validate the effectiveness of our end-to-end pipeline, we compare it to a two-stage variant that first generates multi-view shaded images, followed by a PBR decomposition model. This variant is fine-tuned from our illumination context branch and IDArb (Li et al., 2025b). As shown in Fig. 9a), it often produces inaccurate material predictions, such as excessive metallic- ity, plastic-like surfaces, or overly uniform albedos, likely due to error accumulation across stages. In contrast, our unified design produces more realistic results, showing stronger fidelity for this task. Multi-Branch Generation. To evaluate our disentangled a multi-channel variant that jointly predicts albedo and MR in unified output space, following prior works (He et al., 2025; Zhang et al., 2024a). As shown in Fig. 9(b), this joint prediction often results in inaccurate outputs, especially in metallic regions. Our illumination-guide guided,, material-specific de- sign maintains channel separation and produces more physically and semantically accurate results. Multi- View Illumination Context Branch. We show the importance of illumination context branch by training a material-only variant the same dataset until convergence. As shown in Fig. (c), the variant fails to produce accurate MR maps in metallic regions, resulting in a plastic-like appearance under relighting. The illumination context contributes greatly accurate PBR generation.
mratti-bancch design, we compare it with

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Published as a conference paper at ICLR 2026
Figure 8. Relighting Results. We generate 3D assets from various input images and relight them under diverse environments to demonstrate physical plausibility our PBR textures.
one stage (ours)
Iwo Iwo-slage
multi branch (ours)
multi-hhanel
(a) Oue-stage Generation
(b) Multi-branch Generation.
Reference Image
maps
Albedo
mage
Rendering
Multiview illumination Brunch
Mult-view Lilumination Branch
Multi-view Illumiiaaioon Branch
Figure 9. We ablate the one-stage generation, multi-branch generation, and multi-view illumination context branch (top: metallic, down: roughness) to validate the effectiveness of our method.
CONCLUSION
We present LumiTex, an end-to-end multi-branch pipeline for high-fidelity PBR texture generation. By combining multi-view illumination context branch with novel lighting-aware material atten- tion mechanism, LumiTex enables physically plausible PBR map generation. To ensure global sur- face coverage and coherence, train geometry-guide LVSM for texture inpainting. Extensive experiments demonstrate that LumiTex outperforms existing methods in texture quality, semantic alignment, and relighting fidelity, offering a practical solution for PBR texture generation.
ACKNOWLEDGMENT
We thank the Hunyuan3D team for their inspiring works (Team, 2025a;b; He et al., 2025; Feng al., 2025). The visual design of our teaser is built upon creative assets from Hunyuan3D 2.5. For the ex- perimental comparisons, the 3D assets sourced from the Hunyuan3D platform ensure direct and consistent evaluation. The work was supported in part by Guangdong Provincial Outstanding Youth Fund with No. the NSFC with Grant No. 62293482, the Guangdong Research Projects No. 20172T07X152 and No. 2019CX01X104 the Guangdong Provincial Key Laboratory Future Networks of Intelligence (Grant No. 20228121201000) and the Shenzhen Key Laboratory of Big Data and Artificial In- telligence (Grant No. SPPG20021221173353002 the Guangdong Province Radio Science Data Center.
the Shenzhen Outstanding Talents Training Fund 202002,
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A APPENDIX
A.1 MORE VISUAL RESULTS OF LUMITEX
Multi- View PBR Material Results. To evaluate the generalization capability of LumiTex in es- timating muew G-buffers from in-the-wild images, we present results based on real-captured images (Fig. 10) and Al-generated images (Fig. 11). As illustrated, LumiTex produces consistent multi-view RGB renderings and corresponding materials in both cases, demonstrating strong bustness to images beyond the training distribution. The material renderings are scaled to avoid pure white regions, which may visually blend with the background and hinder clarity.
Results on World Scanned Meshes. We further evaluate LumiTex on real-captured scenes with casual image prompts and noisy geometry reconstructed from photogrammetry. As shown in Fig. 13, LumiTex generates high-quality PBR textures that remain physically plausible and consis- tent across novel views, despite challenging lighting conditions and imperfect inputs. Notably, our model preserves fine details and produces realistic material, highlighting its robustness in real-world scenarios without requiring clean geometry or controlled lighting.
Relighting Comparisons. To demonstrate the quality of our PBR textures, we compare relight- ing results against several state-of-the-art PBR generation methods. As shown in Fig. 12, LumiTex consistently generates realistic,
renderings across diverse scenes and materials, and robustly handles structured patterns like text. In contrast, competing methods often exhibit inaccurate material predictions or texture artifacts under novel lighting. Additionally, we provide supplementary videos demonstrating dynamic relighting, including static objects under rotating en- vironment lighting and rotating objects under fixed illumination. These visualizations showcase the photorealism and physical plausibility of our results.
More PBR Decomposition Results. We report the G-buffer results of the teaser in Fig. 15, contain- ing 38 assets with both real and Al generated meshes and references.
Robustness under Extreme Conditions. To further assess generalization, evaluate LumiTex on inputs exhibiting highly reflective materials and strong backlighting, two challenging scenarios where shading leakage and highlight imprinting are common. As shown in Fig. 16, method consistently maintains clean albedo maps and stable MR predictions without baked-in reflections, while baseline methods struggle under the same conditions.
A.2 IMPLEMENTATION DETAILS
In this section, we elaborate on the implementation details, including the dataset construction and augmentation, the details of the implementation of the multi-view PBR generation transformer, and the ceometry-conditioned LVSM for texture inpainting.
Dataset Details. We curate dataset from Objaverse and Objaverse-XX XL (Deitke et al., 2023b;a), comprising 92K 3D objects for training both our PBR generation model and the large view syn- thesis model. For each object, we sample 6 predefined camera viewpoints with the following ele- aation- pairs for PBR generation transformer training: (20 220,00)) (9), (200,90%) (20", 180%), (20", 270%). }. {90%, 00,,, and (-90%, 0²). Then, we sample 24 random camera viewpoints to train our LVSM-based texture inpainting model. To ensure dense and diverse coverage, each selected view required to differ from all others by at least 5e in either azimuth elevation. Each object is rendered under 3 randomly selected environment maps from lighting database to provide diverse illumination. We use Blender Cycles and Nydiffrast with custom shaders to generate multi-view shaded images and G-buffers (including normal, XYZ, albedo, metallic, and roughness maps). All images are rendered at a resolution of 1024 x 1024 and downsampled to 512 x 512 and 768 768 for multi-scale training.
Data Augmentation. as user-generated content (UGC) platforms where inputs may be Al-generated or casually captured, we employ several commonly used data augmentation strategies. To simulate the variability of user- provided reference images, which often deviate from the canonical viewpoints in our training set, we apply random transformations including scaling, rotation, translation, perspective warping, and lumination scaling with probabilities 0.1, 0.25, 0.25, 0.1, 0.1, respectively.
To enhance the robustness of our method for real-world applications, such
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Reference
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Multi-view rendered images and materials generated by LumiTex. All of the input images are real captured images. We present novel view renderings, along with corresponding albedo, metallic, and roughness maps, from two different views.
Figure 10.
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Figure 11. Multi-view rendered images and materials generated by LumiTex. All of the input images are real Al-generated images. We present novel view renderings, along with corresponding albedo, metallic, and roughness maps, from two different views.
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Figure 12. Relighting Comparisons. Relighting results of state-of-the-art PBR generation methods under two novel lighting conditions. The top row shows the reference images.
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Figure 13. Generalization Real-world Scanned Meshes. LumiTex generates consistent PBR textures and realistic relighting results from real-world images and noisy scanned geometry, demon- strating strong robustness to uncontrolled lighting and diverse inputs. We provide renderings under casual illumination conditions from two different views.
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Figure 14. Failure Cases. LumiTex faces several limitations. We provide the reference image and the generated textures rendered from two different views. First, the current resolution restricts the generation of fine details such as small printed text or specifications (left). Second, the model struggles with transparent materials due to the lack of alpha channel modeling (right).
Training Details of Multi-View PBR Generation Transformer. As introduced in Sec. 4.1 of the main paper, we initialize our model weights from FLUX.I-des (BlackForestLabs, 2024). Then we adapt the model to a geometry-come conditoo-- multi-view image generation generator by improving both multi-modal DiT and multi-view DiT as illustrated in Sec. 3.1. Specifically, to generate multi- view images of N = 6 views, for each view, we concatenate the latent features with geometry tokens and reference image tokens. These inputs are fused using a multi-modal DiT comprising 4 = 19 double-stream transformer blocks. After fusion, we drop the geometry and reference tokens and concatenate the latents from all N views. To encode view identity into each latent, we modify the factorized 3D Rotary Positional Embedding (3D RoPE), indexing each token by its space-view coordinate as (t, 1. (0, h. w) + t x (1,0, 0, o). where t is the view index and is a fixed offset controlling inter-view separation. Finally, the multi-view DiT denoises the aggregated latents using l 38 single-stream transformer blocks. We train the model with the flow matching loss. The feature dimension is C-3072, and the token length is 1024 for resolution of 512 x 512. At each training timestep 4, the model Gø generates diffusion noise Ga T) for image latent I, and the optimization loss Lpor is calculated as:
N Σ
Cptr = E
G
f 11
(1I)
where 1 is the noisy latent of the ground truth shaded image from the i-th view. We first train the multi-view illumination context branch for 20,000 steps. Then we freeze their weights and train the PBR generation transformer for 20,000 steps. We utilize the prodigy optimizer (Mishchenko & Defazio, 2024) to self-adjust the learning rate and correct the bias. The 31, 82 are set to 0.9 and 0.999 respectively. During training, we use batch size of 32 and apply warmup phase of 2,000 steps. We apply gradient clipping at 1.0 and set the guidance scale to 1.0.
Benefiting from the rotary position embedding, our model could be trained with flexible aspect ratios (Esser et al., 2024). We first train our model with the image resolution 512 512. Then, we shift training 768 768 for another 10,000 steps, and employ timestep scheduler with a shift value a 3.0 (Esser et al., 2024).
Training Details of Lased Texture Inpainting Model. The architecture of the texture in- painting model is adopted from the LVSM. which contains 24 full self attentonn transformer layers with image patch size p = 8 and token dimension d = 768.
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To train the texture inpainting model, for each object in the dataset, we randomly sample rendered images from N = 6 viewpoints along with their corresponding geometry images and camera pa- rameters conditions. The model is then tasked with generating the images for M 8 additional randomly selected viewpoints. We train the model with the photometric and perceptual loss, the loss function is calculated
M
Livan
MSE 4) + LPIPS(I,,
(12)
where I; is the predicted novel view images and I; is the ground truth. We train our model for 10,000 steps 8 GPUs using AdamW optimizer (Kingma, 2014) with a learning rate 4e-5. The 31. 82 are set to 0.9 and 0.95, respectively. During training, we use a batch size of 16 and apply warmup phase of 1,000 steps. We also use weight decay of 0.05 on all parameters except the weights of LayerNorm layers, following the original implementation of LVSM. During inference stage, we select M 18 target views from the predefined dense set V, which contains K 48 candidate views.
Inference. We employ RMBG-2.0 (Zheng et al., 2024) isolate the main foreground object from the generated image by removing the background. To prepare the 3D asset, the input mesh is first merged into a single connected component using Blender avoid multiple UV lookups. We then use Xatlas to automatically generate UV parameterizations for material texture assignment. During inference, the guidance scale is set to 3.5. We implement custom inverse renderer to map the generated materials (albedo, roughness, and metallic) from multiple views onto a unified UV texture space, producing ready-to-deploy 3D assets GLB format with physically-basee materials. To address view-dependent variations and ensure view consistency, we apply angle-weighted averaging across overlapping texels during projection. The resulting material maps are stored as UV textures with resolution 2048 x 2048, preserving the fine-grad mene-graindd details from the multi-view material maps. Our model requires approximately 1.5 minutes and 28GB of GPU memory to run inference at a per-view image resolution of 512 x 512, and around 3 minutes with 40GB memory at 768 768.
A.3 LIMITATIONS
Limited Resolution. While LumiTex demonstrates strong performance in generating high-quality PBR textures, several limitations remain. First, generating multi-view images for PBR texture syn- thesis using diffusion transformer (DiT) models is computationally demanding. As a result, our cur- rent implementation is constrained to a per-view resolution of 768 768, which may be insufficient for extremely high-fidelity applications such as small printed text specifications (see Fig. 14), film production or AAA game assets that require detailed textures at 4K or even higher resolution. Scal- ing to such high-resolution outputs presents challenges in both memory usage and inference time. Future work may explore improving the VAE compression ratio, optimizing transformer architec- ture (e.g., via sparse attention), or leveraging cached latents and multi-view parallelism accelerate training and inference to improve quality.
Transparency Modeling. As shown in Fig. 14, current pipeline does not support transparency modeling and thus fails to generate transparent materials such as glass, water, or translucent plastics. This limitation arises from the absence of an alpha channel dedicated transmission parameter in the current material representation. Accurate modeling of these effects would require additional su- pervision, such as refraction-awe aware rendering or alpha/transmitance maps, which are not present in our training data. Future work could incorporate transparency channels and simulate light transmis- sion effects to support broader range of real-world materials.
A.4 LLM USAGE DISCLOSURE
We used Large Language Model (LLM), specifically GPT-4o, solely writing assistant polish grammar, improve clarity, and enhance the fluency of the manuscript. The model was not involved in generating ideas, designing experiments, interpreting results, contributing the core research content. All scientific contributions, methodologies, and analyses were solely developed by the authors. The LLM's role was limited editorial support and did not rise the level of authorship or intellectual contribution.
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Albedo
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Figure 15. G-Buffer Results of Fig. 1. We report the full decomposed PBR results, containing 38
assets with both real and Al generated meshes and references. MR is outlined by silhouettes.
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Figure 16.
Comparisons under Extreme Conditions. We evaluate cases with highly reflective materials and strong backlighting, Our results (left: rendering, middle: albedo, top right: metallic, bottom right: roughness) avoid baked-in reflections and highlight artifacts, demonstrating greater robustness than existing approaches.
Refoneros
(Reil)
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(Alegenened))
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Figure 17. Decomposed Material UVs for Fig. and Fig. 11. We present the decomposed ma- terial UV maps produced by our method. The left panel shows results generated from real rendered images, while the right panel shows results obtained from AI Al-generated reference images.
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Reviews:
[{"review_id": "jfwuSU5H8u", "reviewer": "Reviewer_t6H4", "summary": "The paper proposes LumiTex, a PBR texture generation framework that addresses challenges like material decomposition with limited illumination cues and seamless texture completion. LumiTex combines a multi-branch generation scheme, a lighting-aware material attention mechanism, and a geometry-guided inpainting model to enhance texture quality, realism, and consistency across views. Extensive evaluations are delivered.", "questions": "I would be grateful if the authors could address the above mentioned issues in weakness section and other reviewers' concerns.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- First of all, the work proposes the multi-branch generation design and the lighting-aware attention mechanism offers a novel way of disentangling albedo and metallic-roughness (MR) while integrating illumination context. \n\n\n- Quantitative results (FID, CMMD, LPIPS) and qualitative evaluations indicate that LumiTex achieves competitive or superior performance compared to existing methods, particularly in terms of texture quality and relighting fidelity.\n\n- The authors conducted a wide range of experiments, including comparisons to both open-source and commercial systems, as well as a user study on texture quality, demonstrating LumiTex’s practical advantages in real-world applications.", "weaknesses": "- While the framework employs multi-branch generation and illumination context, similar ideas have already been explored in other recent works. For example, the idea of using lighting priors for material generation is not very novel. The originality of LumiTex comes into question because the combination of multi-view consistency and lighting-guided material attention don't significantly advance the state of the art in a groundbreaking way.\n\n- The method is computationally intensive and limited to generating textures at 768×768 resolution, which severely restricts its application in high-end industries that require 4K or 8K resolution for detailed textures (IMO this is more useful for texture generation for gaming applications or AAA filming). Although the authors propose potential avenues for scaling (e.g., multi-resolution models), scalability remains an unresolved bottleneck. Real-time applications (such as interactive design in AR/VR) would be severely constrained by the current training time of 106 GPU days. The paper does not adequately explore solutions to this scalability issue.\n\n\n- The lack of support for transparent materials. Transparent materials, such as glass, water, and liquids, are commonly required in real-world 3D rendering, especially in architectural visualization and product design. The authors don’t provide a solid argument for why these materials are left out or when this limitation might be addressed. There already exist many works that investigate transparent image / video generation.\n\n- LumiTex doesn’t show sufficient results in handling complex reflective materials or subsurface scattering. These properties are common in materials such as skin, water, and polished metals, which are common in visual effects and games. \n\n- The generalization to novel inputs seems to be limited. While the paper demonstrates robustness with real-world scanned meshes, the model still relies heavily on the type of training data (from Objaverse and Objaverse-XL). The authors did not provide convincing results showing how well LumiTex generalizes to extremely diverse or out-of-distribution inputs. This is a key issue for any system claiming real-world applicability in fields such as gaming or film, where content varies vastly.\n\n- The paper presents failure cases (such as small printed text or transparent materials), but it does not delve deeply into why these failures occur or how they might be addressed in future work. For instance, the lack of alpha channel modeling for transparent materials is acknowledged, but the paper does not offer a roadmap for incorporating transparency modeling or refraction effects, making it seem like a major limitation without any plans for resolution.\n\n- Despite claims of robustness under various illuminations, the framework’s real-world reliability under extreme lighting scenarios (e.g., highly reflective surfaces, strong backlighting) is not well-documented. The existing results are focused on typical lighting conditions, and there is a risk that the model might fail in edge cases involving extreme or uncontrolled lighting, common in cinematic and interactive applications. Without those, the contribution of this paper is doubtful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "b11ecr8oTn", "reviewer": "Reviewer_yPWH", "summary": "The paper proposes a method to generate PBR textures for given objects. The core idea are two stages: from an input mesh + reference image, the goal is to generate N view-consistent PBR images. Here, the core idea is essentially a multi-view image generator with several conditions (they authors refer to this as an illumination-consistent base model); this is then frozen and a material branch is trained for the PBR part. The second stage is a geometry-guided LVSM to synthesize more viewpoints. This is a texture in-painting strategy based on LVSM which generates the extended views, hence, more complete textures. Training is done from 92K objects from Objaverse and Objaverse-XL – for each object, 30 views are rendered (albedo, metallic, roughness, and HDR images). The base model is FLUX.1-dev.\n\nHowever, to be honest, the main technical exposition is quite confusing and it’s not easy to follow the exact details of the multi-view PBR generator (more details below).", "questions": "See above in the weakness sections.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 1, "contribution": 2, "strengths": "- The authors tackle an important problem.\n\n- The renderings of the shaded outputs look nice.\n\n- I appreciate the re-lighting results in the video.", "weaknesses": "The presentation is confusing, and I'm having trouble understanding several of the technical details:\n\n- The introduction mostly pitches the features but a coherent description of the core idea; e.g., how does the multi-view shaded image generator work is somewhat omitted – this makes it hard to read (e.g., first need to read the whole main section and even some of the results to understand which base models they were using)\n\n- Fig 3 is a pipeline but the description of the multi-view illumination-consistent base model is missing, and the pipeline flow is confusing (e.g., where does the input mesh go, where do the reference images come into play).\n\n- I’m confused about the term multi-modal DIT. Seems all input here are images… what are the modes you are referring to here?\n\n- In the video, while the re-lighting looks great, I would’ve loved to see the actual PBR materials rather than the shaded versions. Also the shading in the video seems exhibit some temporal unstable artifacts which is confusing given that the underlying rendering should be just a mesh + PBR texture = this should be visualized.\n\n- The main results are in Fig 6 in the main paper (the majority of visuals does not show the actual PBR results but the shaded versions). Unfortunately, this looks not that impressive. E.g., I would’ve loved to see the PBR textures of the objects in Fig 1 or Fig 5 instead of the shaded outputs.\n\nThere is a general confusing claim to  PBR textures but then consider environment lighting which would still mean that scene specific context is baked in. This is contrary to the definition of a PBR texture.\n\nFigure 2 albedo looks poor. These results look shaded as there is lighting baked in – this is unfortunately not a PBR image / texture. Am I missing something here?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pJN3slcqLl", "reviewer": "Reviewer_RwEh", "summary": "Authors propose an end-to-end framework that, given a reference image and a mesh, generates textures with PBR materials.\n\nMain contributions:\n- Multi-branch design (one for generation of shaded images - to capture illumination context).\n- 2-stage training of multi-view illumination context branch and material branch\n- Lightning-aware material attention mechanism (directly attending to shaded tokens instead of using explicit intermediate images or optimization techniques)", "questions": "1. Please clarify the novelty of the inpainting module. Is the main contribution that you added geometry guidance to LVSM?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "To my knowledge, this is the first approach that is able to generate close-to-true PBR materials, without noticeable baked reflections or highlights. Given strong quantitative and qualitative evaluation, and the importance and complexity of the texturing task, I consider this work to be significant to the field.\n\nThe main contributions and strengths of the paper are clearly demonstrated and ablated in section 4.5, namely:\n- Separate branch for shaded images prediction\n- Single-stage generation (no use of explicit intermediate shaded images)\n- (!) Multi-branch design (instead of multi-channel generation of albedo and MR)\nThis was very insightful to learn, and well-supported by visuals in Fig 9b.\n\nIt was also demonstrated that the pipeline generalizes well to real-world scenes which is helpful in practical applications.", "weaknesses": "- Impact and novelty of geometry-guided inpainting module is limited, although this is not claimed as a main contribution of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i3ZNrQ7VaJ", "reviewer": "Reviewer_EhkN", "summary": "This paper introduces LumiTex, a PBR texture generation model that achieves SoTA quality for PBR texture painting for 3D assets. LumiTex strengthens the connection between illumination shading and PBR material decomposition in the context of 3D generative models. The texture completion via LVSM has also proved to be very effective.", "questions": "The LumiTex DiT is a native multi-view model, implying it should be capable of performing texture inpainting for additional views. I am curious why the authors opted for LVSM instead of leveraging the LumiTex DiT for this task and would appreciate some insights into this decision.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "* The integration of illumination context implicitly through attention layers is a wise design. Additional illumination prior can effectively reduce shading ambiguity in PBR tasks.\n* A joint model for multi-view, multi-channel PBR materials is a good attempt. The results demonstrate its effectiveness in reducing accumulation errors compared to prior multi-stage approaches.\n* LVSM for texture completion performs very well in ensuring global consistency and seamlessness of the final texture maps.", "weaknesses": "I don’t find any major weakness point. Below are a few minor weaknesses points. \n* It is unknown how MR is represented and decoded. Is MR in the orm convention or modeled separately? Is MR latents decoded with the same VAE used for regular RGB images?\n* For the LVSM texture inpainting part, authors seem not to mention how decomposed PBR textures get inpainted. The example (Fig. 4) is on the rendered images.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)