PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors
Authors: Yujin Chen
Yinyu Nie
Benjamin Ummenhofer
Reiner Birkl
Michael Paulitsch
Matthias Nießner
Venue: NeurIPS 2025 poster ()
Keywords: Mesh Texture Super Resolution
Primary area: applications
TL;DR: We present the first zero-shot PBR texture super-resolution framework that outputs high-quality PBR textures from low-resoltuion PBR input.

Abstract: We present PBR-SR, a novel method for physically based rendering (PBR) texture super resolution (SR). It outputs high-resolution, high-quality PBR textures from low-resolution (LR) PBR input in a zero-shot manner. PBR-SR leverages an off-the-shelf super-resolution model trained on natural images, and iteratively minimizes the deviations between super-resolution priors and differentiable renderings. These enhancements are then back-projected into the PBR map space in a differentiable manner to produce refined, high-resolution textures. To mitigate the effects of view inconsistency and lighting sensitivity inherent to view-based super-resolution, our approach incorporates 2D prior constraints across multi-view renderings, enabling iterative refinement of shared upscaled textures. In parallel, we incorporate identity constraints directly in the PBR texture domain to ensure the upscaled textures remain faithful to the LR input. PBR-SR operates without any additional training or data requirements, relying entirely on pretrained image priors. We demonstrate that our approach produces high-fidelity PBR textures for both artist-designed and AI-generated meshes, outperforming both direct  SR models application and prior texture optimization methods. Our results show high-quality outputs in both PBR and rendering evaluations, supporting advanced applications such as relighting.

PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors
Yujin Chen' Yinyu Nie Benjamin Ummenhofer Reiner BirkI? Michael Paulitsch² Matthias NieBner
Technical University of Munich Intel Labs
Rendering with LR texture
Relighting with
Ours
Ours
LR texture
PBR-SR
ERRY
ERRY
PERKY
PERKY
PBR-SR
Figure Given a mesh with fow-resolution (LR) PBR texture maps, PBR-SR generates high- resolution (HR), high-quality PBR textures by leveraging 2D natural image super-resolution priors. Operating directly on PBR maps, including albedo, roughness, metallic, and normal maps, PBR-SR enables realistic relighting of mesh with SR textures under various lighting conditions.
Abstract
We present PBR-SR, novel method for physically based rendering (PBR) texture super resolution (SR). It outputs high-resolution, high-quality PBR textures from low-resolution (LR) PBR input zero-shot manner. PBR-SR leverages off- shelf super-resolution model trained on natural images, and iteratively minimizes the deviations between super-resolution priors and differentiable renderings. These enhancements are then back-projected into the PBR map space in a differentiable manner produce refined, high high-resolution textures. To mitigate the effects of view inconsistency and lighting sensitivity inherent to view-based super-resolution, our approach incorporates 2D prior constraints across multi-view renderings, enabling iterative refinement of shared upscaled textures. In parallel, we incorporate identity constraints directly in the PBR texture domain to ensure the upscaled textures remain faithful to the LR input. PBR-SR operates without any additional training
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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or data requirements, relying entirely on pretrained image priors. We demonstrate that our approach produces high-fidelity PBR textures for both artist-designe and AI Al-generated LR PBR inputs, outperforming both direct SR models application and prior texture optimization methods. Our results show high-quality outputs in both PBR and rendering evaluations, supporting advanced applications such as relighting.
Introduction
High-resolution (HR) textures are essential for achieving realistic visuals in physically based rendering (PBR), particularly in applications where close-up details matter, such as in high-fidelity gaming, cinematic effects, and VR experiences. Unfortunately, many existing assets, such as those older games or legacy 3D models, contain low-resolution textures that result in lower quality render results. Super resolution for PBR textures can significantly enhance the quality of these assets, improving visual clarity and allowing for dynamic relighting and material-aware effects that bring outdated or low-resolution models up to current standards. By directly enhancing low-resolution PBR textures, SR techniques enable better visual fidelity without the need to recreate replace assets, preserving both artistic intent and compatibility with modern rendering workflows.
Super resolution for PBR textures is particularly difficult due to low- and high-resolution (LR-HR) datasets availability specifically for PBR materials. Unlike natural images, where large-scale, paired PBR textures lack such resources, hindering the ability to train models that can accurately upscale these specialized textures. PBR textures require precise alignment across channels (e.g., albedo, roughness, normal) to preserve detail and realism under dynamic lighting and close-up views. However, limited high-quality data hinders the development of effective SR models for such textures.
datasets exist for supervised SR tasks 5 d 23
Existing image SR models can enhance PBR textures by processing every three channels separately. However, current sfate-of-the-ae image SR models are primarily designed for natural images and fail to account for the specific properties of PBR textures [10 15
22
[24 33 34
35 39]. Applying
these models directly to PBR textures often yields suboptimal results, as they overlook the spatial coherence and distinct material properties crucial in 3D rendering. An alternative approach is to apply SR models directly to rendered images. However, this introduces view- and inconsistencies, since these models do not disentangle material properties from appearance. To tackle these challenges, directly optimize PBR textures with differentiable rendering to higher resolution. Once refined, these textures can be seamlessly integrated across different environments, maintaining consistency and computational efficiency similar to their low-resolution counterparts.
We propose a zero-shot PBR texture SR method that relies solely on the input LR PBR maps and a pre-trained image SR model. First, we generate an initial SR texture map by combining interpolation- based upsampling with the pre-trained SR model, establishing strong foundation for further refinement. Second, we apply differentiable PBR rendering on the 3D mesh, synthesizing multi-view renderings from strategically placed camera viewpoints. To enhance high-frequency details, we render images from the same viewpoints and process them with the pre-trained SR model, treating the outputs as sseudo-ground truth (GT) images. We optimize the SR PBR textures by minimizing the discrepancy between differentiable renderings and pseudo-GTs. Leveraging the view- independence of pseudo-GTs, we adopt a robust optimization strategy that jointly updates the target PBR maps and a weighting map for each Pseudo-GT, allowing the model to adaptively downweight unreliable supervision. Additionally, to maintain consistency between the SR and LR PBR textures, we introduce PBR consistency constraints that guide fiew-aware optimization. Extensive experiments validate that our method achieves state-of-ther the-art performance in both texture fidelity and rendering quality, facilitating applications such relighting, which are beyond the capabilities of traditional image SR methods.
In summary, our contributions are as follows:
We present the first zero-shot PBR texture super-resolution framework, effectively leveraging pretrained image SR priors both UV texture space (for initialization) and in the 2D rendering space (for iterative refinement).

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Low-res Renderings
Encoder& & add
Decoder
Image Restoration Labent Diltuiion
Metallic
Aloetoo
Foughness
Weighting Maps
Initializaion
Super-res
PBR Loss
Mutti-view Rasterize
Pixel-wise Loss
Super-res PBR
Renderings
Super-res Renderings
Update
Albedo
Rougrness
Matalicc
Normal
Iterttivlly update
Input
Tralnable
Non-Trainable
Forward
<>>
Loss
C--
Backward
Figure 2: Pipeline Overview. PBR-SR begins with mesh and its LR PBR texture, which are used initialize the target SR texture (Section (.2). Renderings are then generated from properly set cameras and passed through an image restoration latent diffusion model to produce SR renderings as pseudo-GT images (Section 3.4). A differentiable mesh rasterizer generates corresponding renderings at the same resolution as SR pseudo-GT images. A robust pixel-wise loss is applied between these renderings and the pseudo-GTs, while per-view weighting map is jointly optimized to adaptively balance supervision. Additionally, PBR consistency constraints are enforced on the SR textures using the input LR PBR cues. This process iteratively optimizes and refines the SR textures for high-quality results (Section 3.5
We introduce an iterative optimization algorithm that refines high-resolution textures by distilling high-frequency details from image priors, while preserving fidelity to the low-resolution input through tailored PBR regularizations.
2 Related Work
Realistic materials play crucial role in generating detailed and realistic images. To this end, many analytical models have been proposed describing how surfaces reflect light. Early works like 7 29 use simple reflectance models that lack physical principles like energy conservation, which changed with the advent of physically based rendering (PBR) [3 28). While PBR has found wide adoption artist workflows and rendering engines PBR materials with textures remains difficult and time-consuming task in which SR methods tailored to materials can help.
18), the creation or remastering of spatially varying
Image Restoration and Super Resolution. Recent advances in image restoration and super- resolution (SR) have significantly enhanced image quality across various applications (20) 22 40 42 26]. Notable transformer-basee methods, including CAMixerSR (34), HiT-SR (39], and HAT [00. im- prove reconstruction through hierarchical feature fusion and adaptive feature mixing. MambalR employs state-spac models to address complex spatial relationships effectively. Diffusion-barn based approaches such as DiffBIR 24 and StableSR [3] excel generating rich, high-frequency details but typically lack multi-view consistency. Recent works explore multi-view image SR with NeRF- based representations 17 32 43 or Gaussian representations 14 38,, enabling view-consistent super-resolution. In this work, we focus on using natural image priors for PBR mesh texture super resolution.
Mesh Texture Generation. Recent advancements in mesh texture generation have explored GANs
and diffusion models achieve realistic results (9] [T] T6
25
30
36
37
41
Texurify [1]
uses a GAN-based method to generate textures directly on the mesh surface, ensuring consis- tency across views, while Convolutional Generation of Textured 3D Meshes 27 learns generate both meshes and textures using 2D supervision. Diffusion models have also proven effective, as seen Text2Tex [ which synthesizes textures from text descriptions, and Paint-it (36), which combines deep convolutional optimization with physically based rendering for realistic textures. Decorate3D [16] further enhances text-driven texture generation for real-world applications. These works push the boundaries of quality and realism in texture synthesis for 3D models. Different

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from these works that generate texture constrained by category or text prompts, we target using low-resolution texture as important clues and focus on the task of texture map SR.
Mesh Texture Super Resolution. Our review of the literature on mesh texture SR yielded only 21 where the texture map contains baked material and lighting. This method finetunes SR model small paired dataset of LR and HR texture maps and applies it directly for texture upscaling. Unlike this approach, we focus on SR for PBR texture maps, enabling broader applications like relighting and material-aware editing, Similarly, DDcorate3D [ uses a standard SR model to upscale generated 512 512 UV textures to 2048 x 2048 but still includes baked material and lighting. In contrast, our method directly optimizes LR PBR texture maps while considering mesh geometry, producing high-quality SR PBR textures that are compatible with artist-created meshes and integrate efficiently with existing PBR frameworks to improve texture fidelity.
Method
3.1 Overview
PBR-SR aims to recover HR PBR texture maps given the LR PBR texture maps with the corresponding mesh, so that the output SR PBR texture maps can produce high- renderings. We denote the input LR and the output SR PBR texture maps by albedo (T T roughness (T T. metallic (T Ir- TM ) and surface normal (T Th ), respectively. Once we obtain the SR PBR texture maps, we can produce high-quality renderings under arbitrary lighting and viewpoints.
The overview of PBR-SR is illustrated in Fig. 2 We propose a zero-shot method for PBR texture super-resolution by leveraging pretrained image SR models and differentiable rendering. First, we initialize HR texture maps from the given LR inputs. Next, we render multi-view images and upscale them using pretrained SR model to generate pseudo-GT's A differentiable renderer synthesizes images from the same viewpoints using the current HR textures. By iteratively optimizing the textures minimize the designed loss functions, our method effectively enhances PBR texture details.
3.2 PBR Texture Initialization
Given the input of LR PBR texture maps, we use them as the basis to initialize SR PBR maps. Since the albedo map shares the most visual similarity with natural images, the LR albedo map is directly fed to the SR model to produce the SR albedo map T ar For the ARM maps (including ambient occlusion (AO), roughness T and metallic T well the normal map T we apply bicubic interpolation to upsample them to the target resolution. If AO is unavailable, an empty map is allocated in the red channel placeholder. The initial texture maps will be optimized iteratively through the following modules.
3.3 Differentiable Rasterization for PBR
Given the PBR texture maps from Sec. 3.2 we texture the 3D mesh and perform differentiable rasterization to obtain renderings. To render a point on the mesh surface, the albedo ag E R$, roughness 0g R, metallic mg E R, and normal direction n, E RR can be queried from the texture maps using UV coordinates. These UV coordinates are either predefined for the corresponding mesh or computed through UV unwrapping. The specular reflectance Fo.e I is calculated as:
Fo.o = 0.04 (1 - -ma) + mg ag.
The rendered color Lo(p. w) at surface point p. viewed from direction w. is computed using the rendering equation:
Lo(p,w) = S L. wi, w) (we na) dwis
(I)
where w; is the incident light direction, Ω is the hemisphere around the surface normal ng, and L, is the incident light from the environment map. The BRDF fo (p,,w; including albedo ag, specular Fo,e, and normal ng.
wi; w) models the material properties,

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This rendering equation can be decomposed into a diffuse term Do(p) and specular term Sa(p. w)
using the Cook- Toro-Tor microfacet model [3]
Le(p. (()p.w)= = Do(p) + So(p.
Do(p) ao -mo) Li(p, wi)( no)dwi,
DoFoGo
Sp(p.w)= A(W [o)(w,; no)
(P (w) no)dw,,
(2)
where Do, Fo, and Go represent the microfacet distribution, Fresnel term, and geometric attenuation, respectively. Da and Gg depend on roughness agg and F9 based on specularity Fu,a-
Once all surface points are processed, the rendered image In is generated from specific camera. For brevity, we denote the rendering process as I = RM (Td. T Tw. T where (M the differentiable mesh PBR rendering function.
sP
3.4 Super Resolution on PBR Rendering
We also use the PBR texture maps from Sec. 3.2 and perform the traditional undifferentiable mesh rendering to render an image from each specified camera viewpoint. Subsequently, we upscale each rendered image Is with 4x super-resolution scaling using DifBBIR (24), unified blind image restoration framework based on diffusion models. DiftBIR consists of two cascaded stages: first, it removes image degradations yield intermediate high-fidelity restorations; second, it leverages a specially designed IRControlNet module built on latent diffusion models to regenerate realistic figh-frequency details. Specifically, adapt DiffBIR by reducing the denoising steps to five for computational efficiency and modify the text prompts to emphasize PBR rendering characteristics (details provided in Supplementary Materials). The resulting high-resolution image I serve as pseudo-GT target of each viewpoint for optimizing high-resolution PBR textures.
3.5
Iterative Optimization
We use the super-resolution images from Sec. 3.4 to supervise our differentiable renderings from Sec. 3.3 and iteratively optimize the PBR texture maps (T sr. T ap- Tm. T initialized in Sec. 3.2 Optimizing PBR texture maps requires rendering the mesh from diverse viewpoints to capture as much surface detail as possible. To achieve this, normalize the mesh and position cameras surrounding sphere to generate well-distributed set of viewpoints. The camera poses and intrinsics are set to ensure that each rendering captures meaningful surface content across the mesh.
For the initial PBR texture map. we randomly select batch of viewpoints in the first iteration. For each view, render an image using PBR rendering in Sec. 3.4 These rendered images are passed through a pre-trained SR model, which produces the pseudo-Gi ISR
Simultaneously, the same view, we render an image I H R by differentiable rendering in Sec. 3.3 with the same resolution of the pseudo-GTT 1 using the optimizable PBR texture map (T sr. T .xt
T T The loss function update our PBR texture maps consists of two parts: robust pixel-wise loss and PBR constraints.
3.5.1 Robust Pixel-wise Loss
To handle inconsistencies in the Pseudo-GT SR images, propose robust pixel-wise optimization scheme that downweights unreliable pixels via a learnable per-image pixel weighting map (u, v) E [0, 1), where (u. u) denotes a pixel location and indexes the image. We denote the predicted PBR rendering as S E HCCxxX and the target as R The robust pixel-wise loss defined as:
En.. m.D Wi(,vv) ( v) I v
4pix
Σ.. (u,
1=1
This formulation computes a normalized weighted MSE per image and then averages across the batch. Importantly, squaring W.(u, v)² allows sharper modulation of unreliable regions. To regularize the
5

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learned weight maps, we penalize deviation from using per-pixel mean squared penalty:
W Σ Σ (1- (u,))
(4)
HW 1=1
The final robust rendering loss becomes:
Crouust = Apix Lpix + Areg W
(5)
where Apix is global scaling factor and Are is the regularization weight. This design ensures that gradient flow is preserved across all pixels while adaptively reducing the influence of uncertain regions (e.g., view inconsistency and shadows), and aims stabilize PBR learning under noisy
pseudo-ground-ruuth supervision.
3.5.2 PBR Constraints
PBR Consistency Loss: This term ensures consistency between the optimized HR PBR texture maps and the LR texture imput across all material properties. It encourages the refined HR textures to preserve the structure and material integrity of the original inputs while allowing for higher-resolution details:
Cpbr
Σ
WT (I|Pool(Ta) Tirll + Assim CSSI Tir),,
(6)
TET T
where Tg denotes the current HR PBR texture map heing optimized: Th, indicates the corresponding LR textures, and WT denotes the weight of the L1 loss applied between textures. Pool(.) is the average pooling operator with a kernel size equal to the PBR upscaling factor, which downsamples the HR texture match the LR input. Lssim SSIM loss and Assim a weighting factor.
PBR Total Variation (TV) Regularization: To encourage spatial smoothness and suppress undesir- able artifacts in the optimized PBR textures, we introduce total variation loss:
LN
Σ
VI + V,T((,),
TE T, T T..
where V and V, represent the horizontal and vertical gradients of the texture maps, respectively. Minimizing the TV loss promotes smooth, artifact-less textures while preserving important structural details.
The total loss for the optimization is then formulated as:
Listal Lrobus Xpbr Aua Lavs
where Apbr and dep are weighting factors to balance these three loss terms.
4 Results
In this section, we present the experimental setup, evaluation metries, and results obtained from our proposed method for PBR texture super resolution.
4.1 Experimental Setup
Datasets. Since there no established benchmark on mesh PBR texture super resolution, we collect set of PBR meshes with rich texture information 42) (Artist-designed Dataset), This collection contains 16 different high-quality meshes with high-resolution PBR maps 4096 x 4096 or 8192 x 8192 resolutions, and their low-resolution counterparts with a 4x smaller resolution. In addition, we evaluate on Al-generated PBR-textured meshes sourced from the Hyper3D commercial platform 3 (Al-generated Dataset). These meshes contain generated PBR textures with greater variety and realism, and each PBR channel is provided at 512 x 512 resolution. We apply 4x super-resolution to generate 2048 x 2048 outputs. Each model contains set of texture maps that include albedo, metallic, roughness, and normal maps. We use low-resolution meshes as the input to the SR model and the high-resolution counterparts ground truth for performance evaluation.

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Table I: Quantitative comparison of PBR texture maps and renderings PSNR from PBR SR results on the Ariist-esiined Dataset. 'indicates a supervised method finetuned on PBR data. refers an
optimization-aase based method. The red and orange respectively denote the best and the second-best results.
Method
Albedo
Roughness
Metallic
Normal
Renderings
OSEDiff
35
23.495
24.095
26.922
23,957
23.804
DifBBIR 24
24.842
27.563
27.493
24.743
25.495
SwinIR
22
26.990
26.241
28.564
23.410
24.952
HAT 10
25.260
30.559
30.536
23,561
26.205
StableSR 33
25.398
27.648
28.953
24.191
25.489
CAMixerSR [4
26.297
28.273
30.473
24.056
26.791
CAMixerSR-FTT
27.800
30.642
28.961
25.655
27.928
Paint-it SR [36
25.682
29.729
28.955
28.237
23.959
PBR-SR (Ours)
29.731
31.602
31.889
29.088
28.001
Pretrained SR Models. For albedo initialization and generating pseudo-GT (Sec 3.4 Sec 3.5
we utilize DifBBIR [24]. two-stage diffusion-based model that efficiently restores details through degradation removal and detail regeneration. All pre-trained models remain fixed throughout the optimization process, making our framework universally applicable to future models.
Implementation Details. Our implementation leverages the differentiable renderer from [99 to produce rendered images from selected viewpoints. These images are then super-resolved using the same image SR model create pseudo-GT's for optimization. Unless explicitly specified, the same environment lighting setup is used for both optimization and evaluation. The optimization uses the Adam optimizer with constant learning rate of 10- In each iteration, which corresponds to viewpoints. We stop the iterative optimization after 2000 iterations.
batch size of
4.2
Comparison with Baselines
We compare our method with several alternative techniques for PBR texture SR. We evaluate the performance against:
Image SR Methods:
Including SwinIR (22]. HAT [00) CAMixerSR 34], OSEDiff [35, Sta-
bleSR 33 and DiffBIR (24), which are applied directly on the PBR texture maps.
CAMixerSR-FT: Since CAMixerSR achieves overall balanced performance on PBR channels, we use its architecture and pretrained weights from natural images, and then fine-tune it on collection of 24,000 LR-HR PBR map pairs (120 x 120 480 480) comprising diffuse, ARM, and normal maps extracted from the Poly Haven website [4] (no overlap with the evaluation data). Unlike the other baselines and our PBR-SR, CAMixerSR-FT is supervised baseline.
Paint-it it SR: Paint-it (6) is a model designed for text-driven mesh PBR texture generation. We adopt variant of Paint-it as an optimization-bassee baseline for the PBR texture SR task by replacing its -o-maee diffusion model and SDS loss with an image super-resolutin model and render loss function.
Evaluation Metrics. We evaluate the performance of our PBR texture SR method both PBR maps and renderings. We assess the quality of the PBR texture maps on albedo, roughness, metallic, and normal maps individually by comparing the PSNR (Peak Signal-to-Noise Ratio) ground truth high-resolution PBR maps. A higher PNSR value is better. In addition, we compare the rendering quality from novel views (views not used during the optimization process), utilizing PSNR to quantify the difference between renderings from SR PBR results and the ground truth PBR maps.
results with
Quantitative Evaluation. Table a quantitative comparison of various super-resolution methods on PBR maps and final renderings, measured by PSNR The evaluated maps include albedo, roughness, metallic, and normal, alongside the resulting rendered images. The proposed method, PBR-SR, is compared against both supervised baseline (e.g., CAMixerSR-FT optimization siaaze approaches (Paint-it. SR), and state-of-the-art transformer and diffusion- based methods. The proposed PBR-SR consistently outperforms all baselines, achieving the highest scores across all five metrics. While C-., supervised method fine-tuned on PBR data, performs well on Roughness and rendering metrics, it still lags behind PBR-SR. Paint-it SR shows strong results the normal map
7

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XI
HAT
OSEDiff
DifTBIR
Paint-it
CAMixe5R
CAM
Ours
GT
HAT
OSEDiff
DifBBIR
Paint-itSR
CAMiee/SR
CAMixerSR-FT
Ours
GT
HAT
OSEDiff
DIABIR
Paint-itSR
CAMbleISR
CAMixerSR-FT
GT
Figure 3: Comparison of renderings from PBR texture SR results. Our method produces consistently
higher-quality renderings with improved detail.
but falls short on others, especially in rendering quality. Transformer seasrforme---aaeed models like SwinIR and HAT perform competitively in specific channels but struggle with consistent performance across all maps. Overall, PBR-SR delivers the most balanced and superior results, highlighting its effectiveness high-quality material and renderings. method against the two strongest baselines. The trends observed on the Al-generated meshes are consistent with those on the original 16 high-quality test cases. Across both evaluation sets, our method achieves robust improvements in all PBR texture channels and final rendered appearance, further confirming its generalization ability across diverse mesh sources and content types.
Table 2
we provide quantitative results comparing our
Qualitative Comparison on Renderings. In addition to quantitative metrics, we present qualitative comparisons of rendered images using our optimized PBR textures versus those produced by baseline methods. Fig. 3 shows renderings under identical lighting conditions. featuring the Table Clock, Cradle Globe, and the Chinese Chundelier at 4x SR. The LR and GT display the renderings from the LR PBR and GT PBR textures. HAT and CAMixerSR cannot reveal accuracy in all PBR channels, leading to distortion reflection artifacts. OSEDiff, which employs diffusion model, produces high- frequency details, though often diverges from the LR cues, crasing altering LR

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LR
OSEDiff DiffBIR
Paint-it
Ours
Albedo
ARM map
Normal map
Figure 4: Comparison of PBR tiles from 4x PBR SR results of different meshes from our test set. Our method significantly improves the LR PBR all channels and outperforms the inference-based and optimization-baser baselines.
Table 2: Quantitative comparison of PBR texture maps and renderings PSNR from X4 PBR SR results on the Al-generated Dataset. indicates supervised method finetuned PBR data. The red and orange respectively denote the best and the second-best results.
Method HAT
Albedo
Roughness
Metallic
Normal
Renderings
26.368
32.750
31.794
28.235
28.312
CAMixerSR-FFT
31.439
33.195
27.583
31.054
30.841
PBR-SR (Ours)
33.841
35.059
35.352
34.564
31.208
Table Ablation study on our PBR mesh collection (Artist-designed Dataset). PSNR of PBR map channels and resulting renderings are reported.
Setting
Albedo
Roughness
Metallic
Normal
Renderings
w/o PBR Loss
26.116
27.661
29.220
27.130
26.572
w/o PBR TV Regularization
27.929
30.744
31.492
28.507
27.593
w/o Robut Pixel-wise Loss
28.084
30.817
31.734
28.639
27.666
full
29.731
31.602
31.889
29.088
28.001
details, with material property shifts causing incorrect brightness and shading. DiffBIR introduces much noise and struggles get accurate, detailed renderings. CAMixerSR-FT learns to produce plausible outputs in the texture space through supervision on training data. However, this does not guarantee high-quality results in the rendering space. In practice, it often leads to distortions or lack of sharpness in the rendered appearance, as the model is not explicitly optimized for rendering fidelity. Paint-it SR, an optimization-bans based model, iteratively improves PBR renderings but appears more blurred than ours. Our method achieves results that closest to the GT renderings.
Qualitative Comparison on PBR Texture Maps. Fig, visualizes PBR texture tiles, with three rows from top to bottom showing the albedo map, the ARM map (with channels for ambient occlusion, roughness, and metallic), and the surface normal map. From left to right, the columns correspond to LR PBR texture, PBR texture generated by OSEDiff, DifiBIR, Paint-it SR. and Our method PBR-SR, GT HR PBR texture. As shown in the figure, our method significantly outperforms all inference- and optimization-bann based baselines. Especially in comparison with the LR PBR texture, our method demonstrates a substantial improvement texture quality, highlighting the effectiveness of applying image SR priors to the task of PBR texture SR.

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Table 4: Ablation on the image SR model used in our PBR-SR pipeline. We list the quantitative comparison of PBR texture maps and renderings PSNR from x4 PBR SR results.
Method
Albedo
Roughness
Metallic
Normal
Renderings
CAMixerSR 27.793
29.927
31.784
27.294
27.466
StableSR
27.678
31.025
32.258
27.995
26.415
DifßBIR
29.731
31.602
31.889
29.088
28.001
presents an ablation study assessing the contribution of key components in our method. Removing the PBR loss leads to the most significant drop in PSNR across all metrics, particularly in albedo and renderings, underscoring its importance. Excluding TV regularization or the robust pixel-wise loss also results in consistent performance drops, especially in roughness and normal maps. The full model achieves the highest PSNR in all categories. confirming that each component contributes to the overall quality, with the most notable gains in PBR quality and rendering fidelity.
Ablation Study. Table Preeets
Ablation on Image SR Models in PBR-SR. Table we compare three models used in our pipeline for both initialization and rendering super resolution, and report the final optimized SR results of PBR maps and corresponding renderings on the Artist-designed Dataset. DifIBIR achieves the best performance across most PBR channels, including Albedo, Roughness, and Normal, as well as in the final rendering PSNR. Specifically, it outperforms CAMixerSR and StableSR by notable margin in both texture fidelity and rendered appearance, indicating its superior ability to preserve structural details and material realism in the super-resolved outputs. While StableSR performs well on the Metallic channel, it underperforms on others and results in lower rendering quality overall. Moreover, DifBIR is computationally more efficient than StableSR, offering faster inference while maintaining high-qualiny outputs. This makes a favorable choice for integration into our iterative optimization pipeline, where both accuracy and runtime efficiency are critical.
Limitations. While PBR-SR performs well on PBR texture SR tasks, it struggles with severely degraded LR textures due to the limitations of natural image priors. Multimodal cues like text or sketches could help in such cases. The current zero-shot optimization removes the need for training data but is relatively slow, limiting real-time applicability. Future work will explore faster optimization and stronger speespcc priors with multimodal integration.
For additional details and results, please refer to the Supplementary Materials.
Conclusion
We have introduced PBR-SR, the first zero-shot super-resolution method specifically designed for enhancing PBR textures on 3D meshes. By integrating pre-trained image SR models with differentiable mesh rendering, our approach effectively restores detailed textures from low-quality inputs while ensuring accurate preservation of material consistency. PBR-SR significantly improves texture fidelity and rendering quality, outperforming existing state-of- oftee-o-thhe-aa tit-hhean art image SR models and optimization- baselines, without requiring explicit training data. By effectively preserving and enhancing material properties, our method supports downstream applications such as realistic relighting, facilitating more detailed and visually compelling 3D scenes. We believe this work provides valuable insights into leveraging natural image priors for PBR texture restoration, paving the way toward specialized PBR priors in future 3D content generation.
Acknowledgements. This work is funded by the ERC Consolidator Grant Gen3D (101171131), the German Research Foundation (DFG) Research Unit "Learning and Simulation in Visual Computing". Additionally, we would like to thank Angela Dai for the video voice-over.
References
[1] Cgtrader models. https://www.cgtreee
[2] The gltf v2.0 sample
SSmplle---amme---eeeee Assets
[3] Hyper3d models.
ai/
10

---

[4] Poly haven models. hattps://polyhhaen.. com com/models
[5] Eirikur Agustsson and Radu Timofte. Ntire 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 126-135 2017.
[6] Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik. Contour detection and hierarchical image segmentation. IEEE transactions on pattern analysis and machine intelligence, 33(5):898 916, 2010.
[7] James F. Blinn. Models of light reflection for computer synthesized pictures. In SIGGRAPH, pages 192-198, ACM, 1977.
[8] Brent Burley and Walt Disney Animation Studios. Physically- based shading disney. In SIGGRAPH. pages 1-7. ACM, 2012.
191 Dave Zhenyu Chen, Yawar Siddiqui, Hsin- Ying Lee, Sergey Tulyakov, and Matthias Nießner. Text2tex: Text-driven texture synthesis via diffusion models. In Proceedings of the IEEE/CVF International Confer- ence on Computer Vision, pages 18558-18568, 2023.
[10] Xiangyu Chen, Xintao Wang, Wenlong Zhang, Xiangtao Kong, Yu Qiao, Jiantao Zhou, and Chao Dong. Hat: Hybrid attention transformer for image restoration. arXiv preprint arXiv:23399.52239, 2023.
[11] Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl, Michael Paulitsch, Mathias Müller, and Matthias Nießner. Mesh2nerf: Direct mesh supervision for neural radiance field representation and generation. In European Conference on Computer Vision, pages 173-191. Springer, 2024.
[12] Blender Online Community. Blender 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam, 2018.
[13] Robert Cook and Kenneth Torrance. reflectance model for computer graphies. ACM Transactions on Graphics (ToG), 1(1)7-224, 1982.
[14] Xiang Feng, Yongbo He, Yubo Wang, Yan Yang, Wen Li, Yifei Chen, Zhenzhong Kuang, Jianping Fan, Yu Jun, et al. Srgs: Super-resolution 3d gaussian splatting. arXiv preprint arXiv:2404. 10318, 2024.
[15] Hang Guo, Jimmin Li, Tao Dai. Zhihao Ouyang, Xudong Ren, and Shu- Tao Xia. Mambair: simple baseline for image restoration with state-space model. arXiv preprint (tt.::2002.16444 2024.
[16] Yanhui Guo, Xinxin Zuo, Peng Dai, Juwei Lu, Xiaolin Wu, Li Cheng. Youliang Yan, Songcen Xu, and Xiaofei Wu. Decorate3d: Text-driven high-quality texture generation for mesh decoration in the wild. In seveth-e Conference Neural Information Processing Systems, 2023.
[17] Xudong Huang, Wei Li, Jie Hu, Hanting Chen, and Yunhe Wang. Refsr-nerf: Towards high fidelity and super resolution view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8244 8253, 2023.
[18] Brian Karis. Real shading in Unreal Engine 4. In SIGGRAPH Physically Based Shading in Theory and Practice course, 2013.
[19] Samuli Laine, Janne Hellsten, Tero Karras, Ycongho Scol, Jaakko Lehtinen, and Timo Aila. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics, 39(6), 2020.
[20] Christian Ledig, Lucas Theis, Ferene Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz. Zehan Wang. et al. Photo-realistic single image super- resolution using generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4681-4690, 2017.
[21] Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, and Luc Van Gool. 3d appearance super- resolution with deep learning. In Proceedings of the IEEE conference on computer vision and pattern recogmition, pages 9671 1-9680, 2019.
[22] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Lue Van Gool, and Radu Timofte. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international conference on computer vision. pages 1833-1844, 2021.
[23] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolution In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 136 144, 2017.

---

[24] Xinqi Lin, Jingwen He. Ziyan Chen, Zhaoyang Lyu. Bo Dai, Fanghua Yu, Yu Qiao. Wanli Ouyang. and Chao Dong. Diflbir: Toward blind image restoration with generative diffusion prior. In European Conference on Computer Vision, pages 430-448. Springer, 2024.
[25] Minghua Liu, Chong Zeng, Xinyue Wei, Ruoxi Shi, Linghao Chen, Chao Xu, Mengqi Zhang, Zhaoning Wang, Xiaoshuai Zhang, Isabella Liu, et al. Meshformer: High-quality mesh generation with 3d 3d-guided reconstruction model. arXiv preprint arXiv:2408. 101998 2024.
[26] Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön. Controlling vision- language models for universal image restoration. arXiv preprint arXiv:2310.01013 3(8), 2023.
[27] Dario Pavllo, Jonas Kohler. Thomas Hofmann, and Aurelien Lucchi. Learning generative models of textured 3d meshes from real-world images. In Proceedings of the IEFF/CVF International Conference on Computer Vision, pages 13879-13889, 2021.
[28] Matt Pharr and Greg Humphreys. Physically Based Rendering: From Theory Implementation (The Interactive 3d Technology Series). Morgan Kaufmann, August 2004.
[29] Bui Tuong Phong. Illumination for computer generated pictures. Commun. ACM, (8(6)3111-315 1975.
[30] Yawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahendra Kariya, Yanir Kleiman, Emilien Garreau, Oran Gafni, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, et al. Meta 3d assetgen: Text-lo-mesh generation with high-quality geometry, texture, and pbr materials. arXiv preprint arXiv:2407. 02445, 2024.
[31] Yawar Siddiqui. Justus Thies, Fangchang Ma, Qi Shan, Matthias Nießner, and Angela Dai. Texturify: Generating textures on 3d shape surfaces, In European Conference on Computer Vision, pages 72-88 -8. Springer, 2022.
[32] Chen Wang. Xian Wu, Yuan-Chen Guo, Song-Hai Zhang, Yu-Wing Tai, and Shi-Min Hu. Nerf-sr: High quality neural radiance fields using supersampling. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6445 6454, 2022.
[33] Jianyi Wang. Zongsheng Yue, Shangchen Zhou, Kelvin C.K. Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. 2024.
[34] Yan Wang. Yi Liu, Shijie Zhao, Junlin Li, and Zhang. Camixersr: Only details need more "attention". In Proceedings of the IEFE conference on computer vision and pattern recognition, pages 25837-25846, June 2024.
[35] Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. ste-step effective diffusion network for real-world image super-resolution. arXiv preprint arXiv:2406.08177 2024.
[36] Kim Youwang, Tae-Hyun Oh, and Gerard Pons-Moll. Paint-it: Text-to-texture synthesis via deep con- volutional texture map optimization and physically-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4347-4356, 2024.
[37] Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Zhengzhe Liu, and Xiaojuan Qi. Texture generation on 3d meshes with point-uv diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4206 4216, 2023.
[38] Xiqian Yu, Hanxin Zhu, Tianyu He, and Zhibo Chen, Gaussiansr: 3d gaussian super-resolution with 2d diffusion priors. arXiv preprint arXiv:2406. 10111, 2024.
[39] Xiang Zhang. Yulun Zhang, and Fisher Yu. Hit-sr: Hierarchical transformer for efficient image super- resolution, arXiv preprint arXiv:2407.0588 2024.
[40] Yulun Zhang, Kumpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision, pages 286-301, 2018
[41] Yuqing Zhang. Yuan Liu, Zhiyu Xie, Lei Yang. Zhongyuan Liu, Mengzhou Yang, Runze Zhang, Qilong Kou, Cheng Lin, Wenping Wang, et al. Dreammat: High-quality pbr material generation with geometry-and light-aware diffusion models. ACM Transactions on Graphics (TOG), -- 2024.
[42] Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, and Wci-Shi Zheng, Selective hourglass mapping for universal image restoration hased on diffusion model. In Proceedings of the IEEÉ/CVF Conference Computer Vision and Pattern Recognition, pages 25445-25455 2024.
[43] Kun Zhou, Wenbo Li, Yi Wang. Tao Hu, Nianjuan Jiang, Xiaoguang Han, and Jiangbo Lu. Nerflix: High-quality neural view synthesis by learning degradation- driven inter-viewpoin mixer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages
2023.
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In this supplementary material, we provide additional details and results that are not included in the main paper due to space constraints. The attached video offers a brief overview of our method, along with qualitative results demonstrating the performance of PBR-SR.
A
Implementation Details
A.1 Datasets
Artist-designed Dataset. In the main paper, we evaluate quantitative results on a collection of PBR meshes sourced from 224. To better evaluate PBR texture super-resolution performance, we select meshes that contain rich and diverse textures in different PBR channels. Table5]list the meshes and their corresponding website links. For evaluation, we treat the downloaded high-resolution textures as ground truth. If paired low-resolution textures are provided, we use them directly as input to our method. Otherwise, we generate low-resolution inputs by applying Gaussian blur followed by bicubic downsampling on the high-resolution textures. Our usage fully complies with the Royalty Free License terms, as the models are only used internally for testing and are not redistributed in any digital or physical form. The dataset was obtained under its original license and not redistributed here. We used the dataset solely for method evaluation. No training, fine-tuning, or derivative model development was performed using this data.
Table 5: List of 16 Artist-designed meshes used in our evaluation. Each URL links to the correspond- ing mesh page.
No.
Model Name
URL
Chinese Chandelier
heppoillllyaan staven com com//s/chnneee chandelier
Canset
papss//pcbuk buh com Ciuuggr Sumple Assets hewpe//aa//eeeeeeeaaee
3 Damaged Helmet
20 Slaw hi comp K Unvepee 21 Sampe Moddl woreoloooiin comm 21 DVaaggeelleemm
4
Shoulder Stmp
Globe
3966052 2afb 4ec5 81c5 $1c5-120055000000000000 424045021
Under Amnour Volleybull
2mivor wonens novy cenner see volle
Tabie Clock
Papeore wow. trepiin Vi- cer ur (iveEnew wre vene Coe cine per-in Pos-so cowaarry 7921
Medioval Chest
9
Cradle Globe
10
Annuned Mun
model
Lounge
Lounge
mommm oc-eher c-mmordd 10- poy- te--niees
13
Old Table
imppo/ww www.ww............ gtreerenneee 53 www.eiineeeeeeieo...o. wie caair toe--ee-------- mool-mo me---a---a ai--i---
14
Old Stool
Siipson ww. wo cor comt 33 sooee nousehol vineroo caair des sooe 200-ald aid cile
15
Old Chair
otherro
Vintage Cozy Rocking Chair
vinnage -oo--oooo--ng-ooo g-crang-
Al-generated Dataset. To strengthen the evaluation, we add 32 additional meshes from the commer- cial Hyper3D website 31, which features Al-generated PBR textures with diverse content. Table lists the meshes and their corresponding website links. Each sample includes PBR maps at 512 x 512 resolution as input and 2048 x 2048 resolution as ground truth (GT). We perform 4x super-resolution as described in the main paper, keeping all experimental settings consistent with those used for the Artist- meshes. The dataset was obtained under its original license and is not redistributed here.
A.2 Optimization
The mesh is normalized to a unit size during the optimization process. The camera is positioned at a distance of 3.25 units with field of view (FoV) of 10 degrees. We use 750 views in total, sampled from 15 different elevations, with 50 views evenly distributed at each elevation. For rendering evaluation, we use 240 views from 6 elevations, with 40 views sampled from each elevation, ensuring an even distribution across the scene. During optimization, the Xpix is set to 100 and Areg is 0.5 in the robust pixel-wise loss term. The weighting map optimized in resolution of 64 x 64 and is interpolated to W, with the same resolution as the rendering's 's image when calculating Lpxx- We assign different weights to the channels of the PBR texture maps in the PBR consistency loss function: we set the weight to 1.0 for the diffuse Wga, roughness wk', and normal map WK", and 0.1 for the metallic map Wk... The SSIM part is weighted by Assim = 10 for all. The overall PBR consistency loss is given a weight Aphr = 10 and the PBR TV loss is weighted by Aeo = 0.5. The optimization process runs for 2000 iterations. On a single NVIDIA A6000 RTX GPU, optimizing a mesh with PBR textures takes around 30 minutes with 2K-to-8K resolution and less than 8 minutes with IK-to-2K
13

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Table 6: List of 32 Al-generated meshes from the Hyper3D website used in evaluation. Each
URL links to the corresponding mesh page.
No.
Model Name
URI.
Medff226-daac--0b6--811-----6 2024818
2004
3
hlpps//hyperr wta./oodd//aa http://hhggrrdaaaooo/m////////// 3e
hitps://hyner wtw.r//dddda////////////o/
M1612
hilps:/hhyper.
8
hilps:/byyer
S8ea6
9 282244aa144444t044555aaaal-----3
hitps://hyper htw.yoorrd...a//o//////a/aaa///e /ttaa//uuun///111110/44444411110 2011
10 08117766-411-44000222--77771-
tilps://hypr.. wttppee.d..a/ooonnm.. aw/oodnm//eeeecc /ttp://mmmc//111111/100
9a /ocbb99
12
https//hperr5 awrodmm/
13 222t6ux--4660-441111100004444440 hilps://hyper www.odddddn//////o////oooooo/m.a wtw.://wwwuon/aaaaaaadee/--////0 161c secll
https//hyyer.. awt.aaa////d/////a http:///omm////////////400171/30
/5829ab3
14
15 So4-ubbbb-bb--------0000000---6-
h/tps//hhyerrr
16
https//hyper55t httppy/www.....oo//oo//oooooao-o
http://ypprss aurrddn/ http:////.../////000000000000000
18
https//hypprrd wtw.oeddd//rdrm/// hwtoooh///////////355000000
19
http:////rrrrrooooooooooeooooo.. wtt.a////o0000000100101408111110 -acc 10db4boabe
20 1400794-ff0--dall--0f--aaaoa
https.//hyper
http://ee.....adodnnnooooat -1--0000011----0--0000--- -adoe- aal---addaaaaaaaaa aachasc 3088
22
https//hyper.dd wtt.odddaaa////cc/cccc 1tt.rodd/ncc99//-11111----- Socchttt--o----- sddac 2Nebe
https//yper5d httpdd/m/cc991/000008------00000
24
https://yyperaa
25
669966G--662-00444-74444-6-----3 htts.//hyyper htt.:///........////////////0//0
290///
26 555177666-45--444774444444066060
htts://hyperdd.a//dddo.
http. per awr.adodd/nnna wwtp:////eee2111-111111100
29
h61 10330088-uth-009--14229333444433 https://yperrd
30
31 -.9-09t5-c3m--1855-111---6-66---
https:/hyppr
32
http:/hypprr.r http://///yoootta//////ooooootta
resolution offline on average. The optimizable parameters are limited to the high-resolution PBR textures, we directly update them using computed gradients.
A.3 Baselines
We leverage the official implementations of SwinIR (22. HAT I0), CAMixerSR 334
StableSR 33
OSEDiff 35 and DiffBIR (24) as baselines. For N times PBR texture super resolution, we initialize SR PBR texture maps using the corresponding models trained for specific N times super resolution, the model is available with their pretrained weights.
For Paint-it SR, we adopt the core deep convolutional architecture and optimization framework of Paint-it, but substantially modify to suit the super-resolution (SR) task. Specifically, we replace the original text-to-ine text-to-image diffusion model and Score Distillation Sampling (SDS) loss with an image super-resolution model and a pixel-wise render loss. The deep convolutional block is re- initialized to produce zero output initially, enabling it to learn only residual components over the interpolated low-resolution (LR) PBR maps. This residual learning setup allows the model to refine high-frequency details while preserving the wwwfrrequncyy information already captured in the LR inputs. Although Paint-it SR shares a similar optimization framework with the original method, is explicitly re-purposed for SR rather than text-to-texture synthesis. These modifications make it strong baseline, yet our proposed approach surpasses through a more effective integration of SR priors and view-consistent optimization.
For CAMixerSR-FT, we make it a supervised baseline using PBR textures fine-tune the off-the-shelf CAMixerSR weights pre-trained on natural images. We use 19 high- PBR meshes from the Poly Haven website 4 (no overlap with the evaluation data), each containing diffuse, ARM (ambient occlusion, roughness, metallic), and normal maps at resolution of 4096 x 4096. For training data generation, all texture maps are downsampled to 1024 x 1024 using bicubic interpolation. From these, randomly extract 480 x 480 patches as high-resolution targets and their corresponding 120 x 120
14

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Table 7: Ablation on robust pixel loss in our PBR-SR pipeline. We list the quantitative comparison of PBR texture maps and renderings PSNR from x4 PBR SR results.
Method
Albedo
Roughness
Metallic
Normal
Renderings
w/o robust 27.748
30.199
31.826
27.702
27.477
Ours
29.731
31.602
31.889
29.088
28.001
Table 8: Ablation the rendering resolution of pseudo-GT used in our PBR-SR pipeline. We list the quantitative comparison of PBR texture maps and renderings PSNR from x4 PBR SR results. Results of 4x SR the Table Clock mesh reported.
Resolution
Albedo
Roughness
Metallic
Normal
Renderings
128
31.913
30.814
33.375
34.284
22.485
256
32.549
30.737
34.013
34.181
23.841
512
33.476
30.646
34.085
34.900
24.659
768
34.318
30.857
35.803
35.229
24.555
1024
36.077
30.644
36,296
35.620
24.856
downsampled versions as low-resolution inputs. This process yields a total of 24,000 LR-HR texture pairs, which are used to fine-tune the x4 PBR super-resolution model. We fine-tune the pretrained CAMixerSR [34] model on each set (diffuse, ARM, or normal). The model is initialized from the official checkpoint and trained for 50K iterations using the Adam optimizer with a learning rate of 1 x 10-4, weight decay of 1 x 10-5, and (31,32) = (0.9, 0.99). A multi-step learning rate scheduler is used with decay milestones at 20K and 40K iterations and decay factor of 0.5. We adopt an L1 loss in the texture (UV) space with equal weighting and no learning rate warmup. The training conducted with exponential moving average (EMA) enabled, using a decay rate of 0.999. All experiments are performed with strict weight loading from the pretrained model, and the best checkpoint selected based on validation performance. The entire training process takes approximately 9.5 hours.
B Additional Results
Impact of Robust Pixel-wise Loss. In the main paper, we adopt a robust pixel-wise loss to mitigate the impact of artifacts in pseudo-GT renderings, such view inconsistency, lighting variation, and shadow misalignment. To validate the necessity of this design, we conduct an ablation by
replacing robust loss with standard pixel-wise rendering loss: Lpx = -
I 2
where b denotes the number of rendered views and 0 represents the optimizable parameters (i.c.. the super-resolved PBR maps). This baseline assumes uniform confidence across all pixels and ignores image-specific supervision noise. As shown in Fig. 5] using this naive loss often leads to overfitting in unreliable regions such as specular highlights or shadows, resulting in artifacts and inconsistent textures. In contrast, our robust formulation introduces a per-image pixel-wise weight map W, (u, v) [0, 1] that adaptively downweights uncertain pixels. We regularize these weights toward one to ensure stable optimization: Lrbuut Apix Lpix + >reg R(W). This design improves both the stability and quality of optimization, particularly in regions prone to multi-view inconsistencies. As shown in 7] our full model with robust pixel-wise loss achieves consistently higher PSNR across PBR channels and final renderings compared to the baseline using the standard pixel-wise rendering loss on the Artist-designed Dataset. These gains highlight the benefit of adaptively downweighting unreliable pixels during optimization. Fig, presents a comparison of renderings under different lighting conditions from our PBR-SR method and its variant without the robust pixel-wise loss. Without the robust loss, optimization relies on uniform pixel-wise average over multi-view supervision, which often introduces blocky artifacts. This issue is exacerbated by the PBR consistency loss, which operates texture space using average pooling and implicitly assumes equal reliability across all pixels. In contrast, our robust loss adaptively downweights unreliable regions (e.g., shadows or view-specific lighting inconsistencies), resulting cleaner and more physically plausible texture reconstructions.
15

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w/o robust
Ours
Figure 5: Comparison of renderings under different lighting from our PBR-SR and its variant without using robust pixel-wise loss. Both methods significantly improve the rendering quality from the LR PBR texture, while ours achieves high-fidelity by getting sharper and more natural details.
Untextured PBR
PBR-SK Texture
Figure 6: Texture for generated PBR texture. Left: we adopt Paint-it (36) to generate L.R texture and use PBR-SR for x4 SR. Right: compare renderings from LR and Ours from two viewpoints. The two rows are under different lighting conditions.
Ablation on Rendering Resolution in Optimization. We assess the impact of rendering resolution on optimization performance using the Table Clock model 128 128, 256 256. 512 x 512, 768 x 768, and 1024 1024. As shown in Table 8 increasing the resolution of the useudo-groum truth renderings consistently improves performance across most channels. Notably, the Albedo and Metallic maps benefit the most as the resolution increases. Rendering quality also improves steadily, with PSNR rising from 22.49 24.86, indicating that higher-resolution renderings provide more accurate supervision signals during optimization. The Normal map shows consistent gains, reflecting enhanced geometric fidelity, while the Roughness channel exhibits only minor variation, suggesting lower sensitivity to resolution changes. Given the diminishing returns beyond 1024 x 1024 and the significantly increased computational and memory costs at higher resolutions, we adopt 1024 x 1024 as the default resolution for DiffBIR outputs in our main experiments.
Clarification on Robust Pixel-wise Loss (Weighting Map Resolution).. As shown in Table of the main manuscript, we have verified the overall effectiveness of our robust pixel-wise loss. To further analyze the impact of weighting map resolution, we conducted experiments on the ShoulderStrap mesh using different weighting map sizes ranging from 8 to 256. The results are summarized in Table We observe that performance improves steadily from 8 64, with diminishing returns beyond 128. This indicates that moderate-resolution maps (e.g., 64 or 128) are sufficient to capture spatial inconsistencies introduced by the SR model, while higher resolutions offer negligible gains but incur significant memory overhead, as a separate weighting map must be optimized for each rendering view. Therefore, we adopt a resolution of 64 as a practical trade-off between reconstruction quality and computational efficiency.
16

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Table 9: Impact of weighting map resolution on reconstruction performance (PSNR) for the Shoulder- Strap mesh. Moderate resolutions (64 (64-128) achieve good balance between quality and efficiency.
Weight Map Res.
Albedo
Roughness
Metallic
Normal
Renderings
8
34.543
32.501
36.272
26,455
27.838
16
34.641
32.551
35.477
26.685
27.923
32
34.743
32.588
35.198
26.575
28.063
64
34.758
32.615
35.264
25.983
28.163
128
34.770
32.649
35.193
25.766
28.448
256
34.765
32.663
35.214
26.594
28.338
Table 10: Comparison of interpolation methods for initializing PBR texture maps on the Globe mesh.
Metallic/Ronghness Init
Normal Init
Albedo
Roughness
Metallic
Normal
Renderings
bicubic bicubic
bicubic
35.638
32.002
35.343
34.299
36.240
bicubic normalize
35.638
32.033
35.341
32.855
36.243
nearest
bicubic
35.647
31.269
32.756
34.301
35.883
Interpolation Method for PBR Maps. To evaluate the impact of different interpolation strategies for initializing metallic, roughness, and normal maps, we conducted an ablation study on the Globe mesh. The results are summarized in Table [0] Bicubic interpolation for metallic and roughness slightly outperforms nearest-neighhor interpolation, providing smoother transitions that facilitate optimization-eveer for maps with binary-valued regions. For normal maps, bicubic interpolation without normalization yields better performance (PSNR 34.30 vs. 32.86). We attribute this to compression and quantization artifacts commonly present in publicly available normal maps, which result in vector norms slightly below one: post- cost-interpolation normalization amplifies these artifacts and degrades quality. Overall, bicubic interpolation serves as robust and effective initialization strategy, while any minor inconsistencies are further corrected through our optimization process.
Robustness Analysis Degraded LR Textures. To evaluate the robustness of optimization- based method under degraded inputs, we conducted additional experiments on the TableClock mesh using two common types of degradation applied to the LR albedo map: (i) Gaussian noise with standard deviations = {5, 10, 15, 20} (in the 0-255 range), and (ii) JPEG compression with quality factors {80, 60, 40, 20, 10). where lower values indicate stronger compression. For each setting, report both the final PSNR after optimization and the PSNR improvement (A) relative to initialization, for the albedo map and the rendered appearance (averaged over views). Results are summarized in Table [I] Our method consistently improves both albedo and rendering quality, even under moderate severe degradation. Performance gradually decreases degradation increases, which is expected; however, the optimization process continues to yield meaningful improvements over the initialization. Notably, our approach remains relatively robust to JPEG compression, maintaining stable performance even at high compression levels. These findings confirm that while input quality influences the final outcome, our method can tolerate a reasonable range of degradation and still benefit from the optimization process.
Texture SR for Generated PBR Texture. We adopt Paint-it 36 to generate 1024 x 1024 PBR tex- tures from an untextured mesh (Damaged Helmet), and apply PBR-SR to perform 4x super-resolution on the generated PBR maps. As shown in 6 our method successfully introduces high- frequency details that are absent in the low-resolution renderings. However, the performance of PBR-SR on generated textures is inherently limited by the quality of the input. Since the gener- ated textures often lack fine-grained structural cues, it becomes challenging for the SR model to hallucinate or infer additional detail. Moreover, current PBR texture generation methods struggle to produce material properties that are physically consistent and visually comparable to those crafted by professional artists. We believe that future advances in generative PBR modeling could complement PBR-SR effectively. A more realistic and physically plausible generation of initial textures would allow PBR-SR to further enhance detail quality, potentially approaching the fidelity of artist-created PBR assets.
17

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Table 11: Robustness evaluation on degraded LR albedo inputs for the TableClock mesh. The results report PSNR after optimization and the corresponding improvement (A) from initialization.
Degradation
Albedo PSNR
Renderings PSNR
Albedo APSNR
Renderings APSNR
None
33,904
24.429
+8.041
+1.031
Gaussian 5
32.608
24.223
+7.080
+0.989
Gaussian 10
30.199
24.085
+4.332
+0.748
Gaussian 15
27.913
23.826
+2.062
+0.517
Gaussian 20
26.174
23.560
+0.325
+0.322
JPEG 80
33.554
24.282
+6.873
+0.719
JPEG 60
33,292
24.352
+6.325
+0.677
JPEG 40
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Qualitative Results on Composed Scenes. To evaluate PBR-SR on scenes with multiple objects, we composed eight meshes from the CGTrader dataset. Figure compares scene renderings using the input low-resolution (LR) textures and our PBR-SR textures. Our method significantly enhances the details of each object in the scene. Additionally, we compare scene renderings under different environment lighting conditions in Figure 8 We tested three environment maps from Poly Haven 4 Billiard Hall De Balie and Beach Parking The results demonstrate that PBR-SR consistently improves rendering quality across all lighting scenarios.
Discussion on Learning- based and Optimization-basee Methods for PBR Texture SR. While the sorming-based baseline (CAMixerSR finetuned on our PBR dataset) achieves moderate improvements, its performance is limited by the scarcity of large-scale, diverse PBR texture meesh datasets and by image-space space loss functions that fail capture physically based effects such relighting consistency and material fidelity. In contrast, our optimization-bann based approach is data-efficient and directly minimizes rendering-space error through differentiable rendering, leading to superior visual realism and robustness under novel lighting, albeit with slower optimization. We view these two paradigms models provide efficient initialization and inference, whereas optimization-bass refinement ensures physically grounded fidelity. Future research could explore hybrid frameworks that integrate differentiable rendering losses and PBR-specifi regularization into the training of forward networks, combining the strengths of both approaches for high-quality, physically consistent PBR texture super resolution.
as complementary: learning-
Failure Cases. Our model can generally generate HR PBR textures based on the provided LR PBR inputs. The main failure cases we observed occur when the input LR PBR maps are of extremely poor quality, lacking sufficient structural or textural clues. In such scenarios, the model struggles to infer plausible high-frequency details, which leads suboptimal outputs.
Video. We include supplementary video showcasing sihosssaasinss various aspects of our method. The video provides an overview of the approach, a visualization comparing the LR PBR texture maps with the PBR-SR outputs, and results under different relighting conditions. Additionally, it features comparisons with baseline methods and renderings of a composed scene, highlighting the differences between LR textures and PBR-SR textures.
https:/yoanu.kreeeM555MMMLRR
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Rendering with LR PBR Texture
Rendering with Our texture
Figure 7: Comparison of scene renderings from LR textures and PBR-SR textures.
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Lighting: Billiard Hall
Lighting: Balic
Lighting: Beach Parking
LR PBR Rendering
PBR SR Rendeting
LR PBR Rendering
PBR SR Remdering
Figure 8: Comparison of scene renderings from LR textures and PBR-SR textures under novel environment lighting.
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Reviews:
[{"review_id": "OHsdFxMIrx", "reviewer": "Reviewer_rUDa", "summary": "This paper addresses the problem of physically based rendering (PBR) super-resolution (SR) using a zero-shot, optimization-based approach that leverages a pretrained image super-resolution model. The authors use the priors from the image-based SR model to optimize the texture maps in atlas view. Supervision is provided with pseudo-labels obtained from super-resolution renders of the textured object. The loss is effectively computed between those SR renders and renders that are using the upscaled (optimized) PBR maps. Additionally, the authors adopt a weighting scheme with learnable weights (with regularizing term), a low-resolution per-map loss constraint, and a smoothness constraint on the individual loss maps. The results demonstrate the method’s capability to produce sharper and cleaner outputs.", "questions": "- What kind of approach do you use for rendering the images I^HR and I^SR? Are you using point lights or environment maps? Do you randomly sample these? This seems important to mention, as it will affect the optimization process.\n\n- A naive baseline would be to take a pretrained SR model and fine-tune it for the PBR SR task. Specifically, the model would be extended to receive all maps (or maps one by one, in which case there is less to do), and output all maps at HR (or separately). Importantly, you would include a rendering loss. One could argue that such an approach would not suffer from a per-instance optimization process (cf. limitations). Wouldn't the authors agree that this is a relatively straightforward way to avoid the optimization requirement?\n\n- Is bicubic interpolation really the correct sampling method for components such as normal and metalness maps? Metalness maps are generally binary, and normal maps represent unit vector directions.\n\n- Fix the reference at line 69 of the supplementary material: \"As shown in Fig. X\". In Table 3 of the supplementary material, do the results for \"w/o robust\" correspond to the pixel-wise loss applied with the W weight? In this scenario, are the other loss terms (LR PBR loss and smoothness loss) still used?", "limitations": "I appreciate that the authors described some of the limitations and proposed possible directions for improvement. However, it would have been helpful if they had also included examples of failure cases, or at least referred to some already illustrated in the paper. Showing such examples would give a complete picture of the method's flaws.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n- The authors propose a new form of super resolution task, specifically tailored for PBR map upsampling.\n\n- The method yields convincing results in PBR super resolution, especially in the quality and sharpness of the upsampled maps. The rendered views show that the method performs better than the other baselines adapted to PBR super resolution. \n \n\n## Weaknesses\n\n- The paper should be clearer about what is contained in the supplementary material (e.g., types of additional experiments and ablations, experimental specifications, and computational requirements).\n\n- I am not very keen on the notation (k, K). As far as I know, these notations are not commonly used in the literature, though I might be mistaken. Additionally, some notations are rather peculiar, for instance, specular reflectance is usually denoted as F_0 (Cook and Torrance), and using L for the diffuse and specular terms is confusing, as L is already being used for the illumination term itself.\n\n- I am not sure I understand the point of detailing the expression of the BRDF in the main paper. The definition of specular reflection is provided, but there is no clear link made to the BRDF model itself (Eq. 2). For readers unfamiliar with microfacet models, it may not be obvious that the specular reflection term is being used in the Fresnel term.\n\n- The notation for the weighting regularizer (\\mathcal{R}) is not convenient since it is too close to the rendering function (\\mathcal{R}^M). Notations could be significantly improved to enhance the paper's readability.\n\n- Regarding Figure 2: I find the figure a bit cluttered and think it could be clearer. While I had no issues understanding the method from the text, the figure could be more informative. Is AO omitted in Figure 2? Including the notations used in the paper, such as LR, HR, SR, and I^HR, I^SR, would also help.\n\n- Please correct Eq. 6: there is an inconsistency between Tθ (in the equation), T_θ (line 191), and T (in the summation subscript). Also, there is an issue in the description: \"the corresponding L1 texture input\" is unclear.\n\n- Listing \"extensive experiments\" as a standalone contribution is not meaningful, especially since it can be discussed in the introduction. While the authors are not truly introducing a new benchmark, having two clear and focused contributions is perfectly acceptable. This avoids the need for a 3rd point that does not add value as a \"contribution\".\n\n- In Table 1, you identify methods relying on an optimization process with an asterisk, your method does as well. Perhaps highlighting the average runtime or optimization time would be beneficial for a fairer comparison.", "was_revised": true, "final_justification": "The paper proposes a new type of super-resolution task specifically tailored for PBR map upsampling, and the presented method yields convincing results, especially in the quality and sharpness of the upsampled maps. The rendered results show that the method outperforms other approaches and is capable of tackling this new task. The authors answered all my questions raised during the rebuttal phase and were more than open to making every possible change to the overall formatting and notation of the paper. I appreciate the authors for their work and patience. After consideration, I have decided to raise my score to \"accept\".", "rebuttal": "We greatly appreciate the detailed and valuable feedback from the reviewer, and we are glad that our method was found to be effective for PBR super resolution, yielding 'convincing results' with 'better quality and sharpness' than other baselines. We respond to each of the points raised below.\n\n### Weaknesses\n**1. Supplementary material clarity**  \nWe will clarify the supplementary material by revising the main text to explicitly outline the contents, including detailed experimental setups, computational requirements, and additional qualitative and quantitative results.\n\n**2. Notation (k, K), F_0, and L**  \nWe have improved the clarity by adopting standard notations:  \n- The specular reflectance is now denoted as $F_0$.\n- The symbols previously used for diffuse and specular components ($L$) have been changed to $D$ and $S$, respectively, to avoid confusion with $L$, which continues to represent illumination.\n- The notation $(k, K)$is now defined contextually or replaced with more intuitive expressions.\n\nThese changes have been reflected throughout the manuscript, including the equations and figure annotations. We believe these revisions will help make the paper more accessible to readers familiar with standard PBR terminology.\n\n**3. BRDF definition clarification** \nWe have now explicitly clarified the relationship between the specular reflection term and the Fresnel term to better support readers unfamiliar with microfacet-based BRDFs.\n\n**4. Notation of regularizer**  \nTo avoid confusion, the weighting regularizer is now explicitly denoted as $W$, clearly separating it from the rendering function $R^M$. This improves readability and interpretability.\n\n**5. Figure 2 clarity and completeness**  \nWe revised Figure 2 by simplifying the layout, explicitly including Ambient Occlusion (AO), and clearly labeling key components such as LR, HR, SR, $I^{HR}$, and $I^{SR}$.\n\n**6. Equation 6 consistency**  \nWe have corrected Equation 6 for consistent notation throughout. The ambiguous phrase “corresponding L1 texture input” has also been revised to clearly state “the corresponding L1 loss applied between textures.”\n\n**7. Contribution of extensive experiments**  \nWe have restructured the contribution section to focus on two core contributions. The extensive experiments are now described in the introduction to provide context, rather than being listed as a standalone contribution.\n\n**8. Table 1 optimization runtime**  \nWe now have included average runtime for our optimization method, and have clearly annotated runtimes for other optimization-based baselines in Table 1.  \n- Paint-it SR optimization time: around 30 minutes with 2K-to-8K resolution and less than 8 minutes with 1K-to-2K. Similar optimization time as PBR-SR (Ours).\n- CAMixerSR-FT fine-tuning time: 9.5 hours of training. \n---\n### Questions\n**1. Rendering approach**  \nWe use Nvdiffrast as our differentiable renderer (line 217), and a single environment map for illumination (line 220) during the default optimization process. The environment lighting is not randomly sampled, and our default setting uses one fixed HDR environment map. We will release the code and related data.\n\nTo assess robustness, we have tested with over five different environment maps. Each environment map is used individually, and the results remain consistent, as long as extreme lighting (e.g., pitch-black or overly bright) is avoided.\n\nWe have further tested the training with 2 or 3 different environment maps per sample (i.e., multiple renderings per step). Even with the increased diversity, the performance is still stable.\n\nThese results suggest that our method is robust to moderate lighting variation, thanks to the strong guidance from the LR PBR maps. This has been clarified in the revised manuscript.\n\n**2. Naive baseline using pretrained SR fine-tuning**  \nIn our paper, we compare three types of pipelines:  \n- (1) *Direct inference* using pretrained 2D SR models on individual maps.  \n- (2) *Fine-tuning* a 2D SR model on low-resolution PBR texture data.  \n- (3) *Our optimization*, which does not rely on any external PBR dataset and optimizes only on the given textured mesh.\n\nOur method is, to the best of our knowledge, the first to address this problem setting, without any task-specific training, relying only on 2D natural image SR priors. This allows us to support arbitrary shapes and materials without requiring curated PBR datasets.\n\nWe agree that your proposed approach, pretraining an SR model on texture data and using it to initialize our optimization per channel, could be a promising extension that better leverages available data. It offers a good trade-off between generalization and instance-specific refinement. We see this as a valuable direction for future work. We have clarified this discussion in the revised manuscript.\n\n**3. Interpolation method for normal and metalness maps**  \n\nWe appreciate the reviewer raising this point. We conducted experiments to compare different interpolation methods. Below are the results on the *Globe* mesh:\n\n| Metalness and Roughness Init | Normal Init           | Albedo | Roughness | Metallic | Normal | Renderings |\n|----------|------------------------|--------|-----------|----------|--------|------------------|\n| bicubic  | bicubic                | 35.638 | 32.002    | 35.343   | 34.299 | 36.240           |\n| bicubic  | bicubic + normalize    | 35.638 | 32.033    | 35.341   | 32.855 | 36.243           |\n| nearest  | bicubic                | 35.647 | 31.269    | 32.756   | 34.301 | 35.883           |\n\n- **Metalness and Roughness maps**: Bicubic performs slightly better than nearest-neighbor, mainly because of smoother transitions that help optimization, even for binary-valued maps.\n- **Normal maps**: Bicubic interpolation without normalization provides better performance. Interestingly, direct bicubic interpolation yields slightly better performance (i.e., PSNR 34.299 vs. 32.855). Upon investigation, we found that publicly available normal maps often have pixel vectors with norms slightly less than 1 due to compression and quantization artifacts. Normalizing after interpolation may amplify artifacts and degrade quality.\n\nOverall, bicubic interpolation serves as a robust and effective initialization, and any minor issues are corrected through our optimization process. These findings have been discussed in the manuscript.\n\n**4. Supplementary material corrections**  \n- The incorrect reference at line 69 (\"Fig. X\") has been fixed.  \n- In Table 3, \"w/o robust\" refers to using pixel-wise loss without the robust weighting term. Other losses are still applied. This has been clarified in the Appendix (lines 63-75).\n\n### Limitations\nWe have added failure case examples in the supplementary material, including cases with poor LR texture inputs, to give a full picture of the method’s limitations.\n\n### Formatting and Ethical Concerns\n\nWe have revised the bibliography for consistent formatting and will remove potentially identifying elements such as voice-over mentions in the future.\n\nWe have clarified the above points in the revised manuscript."}, {"review_id": "mbeEjuLE3H", "reviewer": "Reviewer_45eH", "summary": "This paper presents PBR-SR, a zero-shot method for generating high-resolution PBR texture maps on 3D meshes. The approach addresses the challenging problem of enhancing low-resolution PBR textures by leveraging pretrained 2D image super-resolution models without requiring additional training data. The method works by initializing high-resolution textures from low-resolution inputs, generating multi-view renderings that are processed through a pretrained SR model to create pseudo GT images, and then iteratively optimizing the PBR textures using differentiable rendering to minimize discrepancies between rendered images and pseudo-GTs. \n\nThe paper's main contributions are:\n- A robust pixel-wise loss with learnable weighting maps to handle view inconsistencies.\n- PBR consistency constraints to preserve material properties.\n- A comprehensive evaluation demonstrating good performance over direct application of image SR methods and optimization-based baselines.", "questions": "Please refer to weaknesses for more details.", "limitations": "While the authors do include a limitations paragraph, it is quite brief and doesn't adequately address several important limitations. More discussion should be included: \n\n- More detailed analysis of computational requirements and scalability limitations.\n- Discussion on failure cases/modes.\n- Discussion on the method's dependence on the pretrained 2D SR model and how the the method performs across different material types.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths\n- The paper is well-written and easy to follow.\n- The paper presents a well-motivated method that bridges 2D image priors and 3D PBR texture enhancement. The use of differentiable rendering combined with iterative optimization is reasonable. The pixel-wise loss with learnable weighting maps is a practical solution to handle view inconsistencies. The PBR consistency constraints effectively preserve material properties while allowing detail improvement.\n- The method achieves better results comparing against multiple strong baselines. The ablation study clearly demonstrates the contribution of each component.\n\n### Weaknesses\n- The evaluation is conducted on only 16 meshes, which represents a small test set for validating method's generalization. While the authors mention the data are collected from multiple sources, the diversity in terms of material types, geometric complexity, and texture details should be discussed. Besides, it is strongly recommended to evaluate the method on more comprehensive benchmarks.\n- The method requires iterative optimization with 2000 iterations and multiple view renderings per iteration which the authors acknowledge as \"relatively slow.\" However, the paper lacks detailed computational analysis. Lack of timing comparisons and memory usage with respect to mesh complexity and texture resolution makes it difficult to understand computational costs for practical usage.\n- The method adopts \"zero-shot\" paradigm. Such paradigm is typically sensitive to input quality. However, the paper doesn't provide analysis of how the method performs when LR textures exhibit various forms of degradation. It is strongly recommended to add robustness experiments by adding different types of noise to LR textures and analyzing how they affect model performances.\n- The method replies on pretrained SR models, which may limit its effectiveness on PBR textures that differ from natural images. The paper doesn't evaluate how this dependency affects performance across different material types.", "was_revised": true, "final_justification": "Thank you to the authors for their comprehensive efforts in addressing my concerns during the review process. The additional clarifications and explanations have significantly improved my understanding of the work, particularly regarding the robustness analysis on degraded LR textures and the detailed discussion of failure modes and limitations.\n\nBased on these substantial improvements in technical clarity, I am raising my score for this submission. I strongly recommend that the authors incorporate these detailed explanations into the main paper rather than keeping them solely in the rebuttal responses. Specifically, the robustness analysis on degraded LR textures and the comprehensive failure mode analysis would greatly benefit readers' comprehension of the technical contributions and practical considerations.", "rebuttal": "We appreciate the thoughtful review. We are pleased that our method was found to be 'well-motivated' and 'practical', and that both the effectiveness of our design and the clarity of our presentation were appreciated. We provide below a point-by-point response to each concern.\n\n\n**1. Expanded evaluation**\n\nTo further strengthen the evaluation, we have added 32 additional meshes from the commercial Hyper3D website, which features AI-generated PBR textures with more diverse content. Each sample includes PBR maps at 512×512 resolution as input, and 2048×2048 resolution as GT. We perform 4× SR as in the main paper. All experimental settings remain consistent with those used on the original 16 meshes.\n\nBelow we provide preliminary results comparing our method against the two strongest baselines. Results from other baselines and visualizations will be added in the final version. Our method consistently outperforms both baselines across all PBR channels and in rendering quality.\n\n\n| Method           | Albedo | Roughness | Metallic | Normal | Renderings |\n|------------------|--------|-----------|----------|--------|------------------|\n| HAT              | 26.368 | 32.750    | 31.794   | 28.235 | 28.312         |\n| CAMixerSR-FT     | 31.439 | 33.195    | 27.583   | 31.054 | 30.841           |\n| PBR-SR (ours)    | 33.841 | 35.059    | 35.352   | 34.564 | 31.208           |\n\nImportantly, the trends observed on the newly added AI-generated meshes are consistent with those on the original 16 high-quality test cases. Across both evaluation sets, our method achieves robust improvements in all PBR texture channels and final rendered appearance, further confirming its generalization ability across diverse mesh sources and content types.\n\nWe will include the complete benchmark results and extended comparisons in the final manuscript.\n\n\n**2. Computational analysis**  \n\nWe provide a detailed discussion in Appendix Section A.2, where we report runtime across different resolutions:\n- On a single NVIDIA A6000 GPU, optimization takes approximately **30 minutes** for 2K-to-8K resolution.  \n- For 1K-to-2K resolution, the process takes **less than 8 minutes** on average.\nAs our method is the first to tackle mesh PBR texture super-resolution without requiring external PBR texture datasets, we adopted an optimization-based approach that requires iterative updates. This design allows us to flexibly enhance high-resolution materials for arbitrary 3D assets without retraining or domain-specific data.\n\nOur method is intended for offline SR of mesh PBR textures, and the optimized outputs can be directly loaded for real-time rendering in downstream applications. While the current optimization time is acceptable for offline use, we agree that improving efficiency is a valuable direction for future work. \n\n**3. Robustness analysis on degraded LR textures**\n\nTo assess the robustness of our optimization-based method under degraded inputs, we conducted additional experiments with two common types of degradation:\n- Gaussian noise added to the LR albedo map, with σ = 5, 10, 15, 20 (in 0–255 range);\n- JPEG compression artifacts to the LR albedo map, with quality factors = 80, 60, 40, 20, 10 (lower values indicate stronger compression).\nWe report both the final PSNR after optimization and the PSNR improvement from initialization (i.e., before vs. after optimization), for both the Albedo map and the rendered appearance (average over views)  on the *TableClock* mesh.\n\n| Degradation     | Albedo PSNR | Renderings PSNR | Albedo ΔPSNR from Optimization | Renderings ΔPSNR from Optimization|\n|-----------------|-------------|------------------|---------------|-------------------|\n| None            | 33.904      | 24.429           | +8.041        | +1.031            |\n| Gaussian 5      | 32.608      | 24.223           | +7.080        | +0.989            |\n| Gaussian 10     | 30.199      | 24.085           | +4.332        | +0.748            |\n| Gaussian 15     | 27.913      | 23.826           | +2.062        | +0.517            |\n| Gaussian 20     | 26.174      | 23.560           | +0.325        | +0.322            |\n| JPEG 80         | 33.554      | 24.282           | +6.873        | +0.719            |\n| JPEG 60         | 33.292      | 24.352           | +6.325        | +0.677            |\n| JPEG 40         | 32.979      | 24.323           | +6.092        | +0.648            |\n| JPEG 20         | 32.138      | 23.853           | +6.278        | +0.828            |\n| JPEG 10         | 30.974      | 22.978           | +6.006        | +0.887            |\n- Our method consistently improves both Albedo and rendering quality, even under moderate or even strong degradation.\n- Performance gradually decreases as degradation increases, which is expected, but the optimization process still provides meaningful improvements over the initialization.\n- Notably, our method remains relatively robust to JPEG compression, showing stable performance even at high compression levels.\nThese results confirm that while input quality does influence the final outcome, our method can tolerate a reasonable range of degradation and still benefit from the optimization process. We will include these findings in the supplementary material.\n\n**4. Dependency on pretrained SR models and material diversity**\n\nThe core objective of our work is to investigate how to robustly enhance PBR texture maps by leveraging natural image priors, without requiring large-scale PBR texture datasets.\n\nTo mitigate domain gap issues, we introduce a rendering SR module that guides the optimization through rendered supervision and uses LR PBR maps as structural constraints across channels. This design allows our method to adapt and refine outputs for diverse material types without retraining.\n\nOur test set includes a wide variety of material types (e.g., metals, plastics, woods, fabrics), and results in **Table 1** show consistent improvements across all PBR channels, demonstrating the effectiveness of our strategy.\nAdditionally, we conducted an ablation study using multiple pretrained 2D image SR models (DiffBIR, StableSR, and CAMixerSR), as shown in Appendix Section B: \"Ablation on Image SR Models in PBR-SR\" and Table 4. While DiffBIR yields the best overall performance, all models contribute to noticeable improvements in PBR texture quality, confirming the robustness of our pipeline regardless of SR model choice.\n\n**5. Discussion on failure cases/modes**\n\nOur model is generally able to generate HR PBR textures based on the provided LR PBR inputs. The main failure cases we observed occur when the input LR PBR maps are of extremely poor quality, lacking sufficient structural or textural clues. In such scenarios, the model struggles to infer plausible high-frequency details, which leads to suboptimal outputs. We have included representative failure case examples in the supplementary material to provide a clearer view of the method’s current limitations.\n\nWe have further clarified this point in the revised manuscript to emphasize generalizability across material types."}, {"review_id": "m6nSRwSNQE", "reviewer": "Reviewer_MDkT", "summary": "This paper presents PBR-SR, an optimization-based method for mesh PBR texture super resolution.\nIt is the first paper to consider the task of PBR texture super resolution, which is important in game and multi-media industry.\nIt leverages the 2D priors from image domain super resolution model and distill the knowledge into UV domain SR task.\nThe main contribution of the paper is a zero-shot texture super resolution framework, and an optimization method with proposed losses.\nGood quality SR results are shown in the experiment part.", "questions": "1. More details should be discussed about the mechanism of the weight map.\n2. More cases should be shown.\n3. The implementation of the baseline method Paint-it SR should be clarified.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n\n- The paper identifies the critical industrial demand for PBR texture super-resolution and, for the first time, proposes a solution to address this challenge.\n- A novel distillation approach is proposed to transfer knowledge from the natural image domain to the UV domain, enabling zero-shot capability in a training-free manner. This effectively solves the problem of the lack of LR-HR PBR texture data pairs.\n- The paper is well written, and the structure is well organized and easy to follow.\n- The presented method is simple to implement in real-world applications.\n- Good super-resolution results are shown, proving its superiority.\n\nWeaknesses\n\n- The mechanism of the weight map (the robust pixel-wise loss) seems unclear to the reviewer. The reviewer understand the additional solution space introduced can reduce the influence of the inconsistency produced by the SR model. However, the 64x64 resolution is not enough for such inconsistency. More detail should be discussed.\n- The choice of the light condition during rendering should be clarified since it is important for optimizing ARM and normal maps.\n- In the evaluation part, only 16 cases are used for comparison. This number is too small to prove the method's generalizability. The reviewer would like to see more cases. Also, since the authors claim that the method can process AI-generated meshes, it would be better to show more cases from that domain.\n- The impact of different natural image super-resolution models on the final results has not been discussed. Does the method rely on DiffBIR or any other SR method that can be plug-and-use (which would be a great advantage of the method)?\n- The implementation of the baseline method Paint-it SR is not clearly described. It seems to use a similar optimization framework to the proposed method and only replaces the optimization target with a U-Net, which produces the PBR residue. I understand that in the original Paint-it paper, neural reparameterization is used for frequency scheduling. However, in the SR task, low-frequency information is already known (LR texture), and it seems redundant to add another U-Net residue. The authors should discuss the main difference between it and the proposed method.", "was_revised": true, "final_justification": "The response has addressed my concerns.", "rebuttal": "We appreciate the detailed and valuable feedback from the reviewer, and we are glad that our method was recognized as addressing a 'critical industrial demand' with a 'novel distillation approach'. We also appreciate that our work was found to be 'well written', 'easy to follow', and 'simple to implement', while demonstrating 'good super-resolution results'. We respond to each of the points raised below.\n\n**1. Clarification on robust pixel-wise loss (weighting map resolution)**\n\nAs shown in Table 2 of the main manuscript, we verify the overall effectiveness of this loss. To further analyze the impact of resolution, we conducted experiments using different weighting map sizes (8 to 256)  on the *ShoulderStrap* mesh. The results are summarized below:\n\n| Weight Map Res. | Albedo | Roughness | Metallic | Normal | Renderings |\n|------------|------------|---------------|--------------|------------|-------------|\n| 8                   | 34.543     | 32.501        | 36.272       | 26.455     | 27.838                |\n| 16                  | 34.641     | 32.551        | 35.477       | 26.685     | 27.923                |\n| 32                  | 34.743     | 32.588        | 35.198       | 26.575     | 28.063                |\n| 64                  | 34.758     | 32.615        | 35.264       | 25.983     | 28.163                |\n| 128                 | 34.770     | 32.649        | 35.193       | 25.766     | 28.448                |\n| 256                 | 34.765     | 32.663        | 35.214       | 26.594     | 28.338                |\n\nWe observe that performance improves steadily from 8 to 64, with diminishing returns beyond 128. This suggests that moderate-resolution maps (e.g., 64 or 128) are sufficient to capture useful spatial inconsistencies introduced by our SR model. Higher resolutions offer marginal gains but incur significantly more memory overhead, as a separate weight map is optimized and stored for each rendering view. Hence, we adopt a resolution of 64 as a practical balance between quality and efficiency. These clarifications have been added to the revised manuscript.\n\n**2. Choice of lighting conditions**\n\nWe utilize a single environment map in our experiments, as stated in the manuscript (line 220). Further experiments with varying and multiple environment maps demonstrated that the results were robust under moderate lighting changes, primarily due to constraints imposed by LR PBR maps, rendering the method insensitive to variations in illumination during optimization. This clarification has been added to the manuscript.\n\n**3. Evaluation scale and AI-generated meshes**\n\nTo address the reviewer’s concern and further validate generalizability, we have added 32 AI-generated PBR-textured meshes sourced from the Hyper3D commercial platform. These meshes contain procedurally or AI-generated PBR textures with greater variety and realism, and each PBR channel is provided at 512×512 resolution. We apply 4× super-resolution to generate 2048×2048 outputs. The experimental protocol remains consistent with that used in the main 16-mesh evaluation.\nWe present preliminary results comparing our method with the strongest baselines. Final results will be included in the revised manuscript, along with comparisons to other baselines and additional quantitative comparisons.\n\n| Method           | Albedo | Roughness | Metallic | Normal | Renderings |\n|------------------|--------|-----------|----------|--------|------------------|\n| HAT              | 26.368 | 32.750    | 31.794   | 28.235 | 28.312         |\n| CAMixerSR-FT     | 31.439 | 33.195    | 27.583   | 31.054 | 30.841           |\n| PBR-SR (ours)    | 33.841 | 35.059    | 35.352   | 34.564 | 31.208           |\n\nThese additional evaluations on AI-generated PBR textures reinforce the general applicability and robustness of our method, beyond handcrafted or artist-designed assets.\n\n**4. Impact of different super-resolution models**\n\nWe provide the analysis of the impact of different SR models in Appendix Section B *Ablation on Image SR Models in PBR-SR*, along with Table 4, where we compare three different SR models (DiffBIR, CAMixerSR, and StableSR) used for both initialization and rendering-time SR.\n\nOur results show that DiffBIR consistently achieves the best performance across most PBR channels, including Albedo, Roughness, and Normal, as well as in final rendering PSNR. It outperforms CAMixerSR and StableSR by a notable margin in terms of both texture fidelity and perceptual rendering quality. While StableSR performs well on the Metallic channel, it underperforms on others and leads to lower overall rendering quality.\n\nImportantly, our method is not tied to DiffBIR—it supports plug-and-play integration of other SR models. This flexibility is a key strength of our pipeline. However, we adopt DiffBIR as the default due to its strong balance of performance and efficiency: it not only yields high-quality outputs but also offers faster inference than StableSR, which is especially valuable in our iterative optimization process.\nWe have clarified this point in the revised manuscript to emphasize the generality of our method with respect to different SR backbones.\n\n**5. Implementation details of  Paint-it SR baseline**\n\nIn Section 4.2 and Appendix Section A.3, we retain the core convolutional architecture and optimization framework of Paint-it, but make the following key changes:\n- We replace the original text-to-image diffusion model and Score Distillation Sampling (SDS) loss with an image super-resolution model and a pixel-wise render loss.\n- The deep convolutional block is re-initialized to output zero initially, allowing it to learn only the residual components over interpolated LR PBR maps.\n- This residual learning setup allows the model to refine details while preserving the low-frequency information already present in the LR maps.\nWhile Paint-it SR shares a similar optimization framework, it is explicitly re-purposed for SR rather than text-to-texture synthesis. These modifications make it a strong baseline, yet our proposed method still outperforms it due to a more effective integration of SR priors and view-consistent optimization.\n\nWe have clarified the above points in the revised manuscript."}, {"review_id": "hqPFYt8kfJ", "reviewer": "Reviewer_h7Gq", "summary": "This paper presents PBR-SR, a super-resolution framework for PBR textures. The core idea is to reconstruct high-resolution PBR texture maps using a pre-trained diffusion prior combined with differentiable rendering-based supervision. PBR-SR leverages an off-the-shelf super-resolution model trained on natural images, and iteratively minimizes the deviations between the diffusion prior outputs and differentiable renderings. Experimental results demonstrate strong performance, although some issues deserve further discussion.", "questions": "Please refer to the Weakness section. I do not consider an additional experiment strictly necessary, but I believe a thoughtful discussion of the trade-offs between learning- and optimization-based inference is important. If the authors provide additional experimental evidence, I would consider increasing my score.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The paper addresses a practical and less explored problem: super-resolution of PBR texture maps on 3D meshes, while maintaining fidelity under relighting. The motivation is clear and relevant to realistic asset generation workflows.\n2. The proposed framework combines a pre-trained diffusion prior with differentiable rendering-based iterative optimization. Along with the use of PBR loss, TV regularization, and pixel-wise supervision, the method significantly improves performance on PBR super-resolution, as shown in comprehensive experiments.\n\nWeaknesses:\n1. The proposed method relies on an optimization-based inference pipeline, which requires per-input optimization and can be relatively slow at test time, limiting its practicality in real-world deployment. \nWhile CAMixerSR is fine-tuned on PBR textures as a learning-based baseline and does achieve improved results, the performance gain is relatively modest compared to the proposed optimization-based approach. However, the paper does not analyze whether this reflects a fundamental limitation of learning-based methods in this setting, or whether it is due to other factors such as loss function design. \nA more in-depth discussion (or experiments, if possible) would help clarify the trade-offs between learning- and optimization-based approaches in terms of performance and inference efficiency.", "was_revised": true, "final_justification": "I have read the rebuttal and appreciate the authors’ clarifications. They have addressed my main concerns, and I am increasing my score from 4 to 5.", "rebuttal": "We sincerely thank the reviewer for the constructive feedback. We are glad that our work was recognized as addressing a 'practical and less explored problem' with 'clear motivation' and that our combination of diffusion priors, differentiable rendering, and pixel-wise supervision was found to 'significantly improve performance'. Below, we provide detailed responses to the concerns raised.\n\n**1. Optimization-based inference pipeline is relatively slow**\n\nThe optimization-based inference pipeline in PBR-SR can be slower compared to fully feed-forward models. This design choice is motivated by our aim to achieve the highest possible fidelity under novel lighting conditions and get rid of extra PBR data reliance, which current learning-based models struggle to generalize to due to domain shift and limited training data. While this makes PBR-SR better suited for offline content creation (e.g., game asset preparation or digital twin generation), real-time deployment could benefit from more efficient alternatives.\n\n**2. Limitation of learning-based methods, or if the design can be improved**\n\nWhile our current learning-based baseline (CAMixerSR fine-tuned on our PBR dataset) achieves only modest improvements, we do not attribute this to an inherent limitation of learning-based methods. Rather, we believe that two key factors limit their current performance:\n- Data limitation: The lack of large-scale, diverse PBR texture datasets paired with 3D mesh geometry under varying lighting conditions makes it difficult for learning-based methods to generalize effectively. Our current fine-tuning is performed on relatively limited PBR data, which likely restricts performance.\n- Loss design: Existing loss functions in image space (e.g., pixel-wise or perceptual losses) do not adequately capture physically-based rendering characteristics, such as relighting consistency or material-specific shading effects, especially under novel views or illumination.\n\nLooking ahead, we believe our optimization-based framework provides valuable insights for improving learning-based methods. Specifically, it opens up the possibility of incorporating physically motivated constraints, such as differentiable rendering loss and PBR-specific regularization, into the training pipeline. \nFor example, when only PBR textures are available, 2D supervision can be used as a proxy. When PBR texture-mesh pairs are available, our differentiable rendering loss can be integrated into the training loop to guide learning with richer, geometry-aware supervision.\nWe see this as a promising future direction to combine the efficiency of feed-forward inference with the fidelity of physically-grounded supervision.\n\n**3. Trade-offs between learning- and optimization-based approaches**\n\nLearning-based methods (e.g., CAMixerSR-FT) offer fast inference but rely on large-scale paired data and are typically supervised in texture space. As discussed in Lines 255–268, this can lead to distortions or a lack of sharpness in rendered appearance under novel lighting, since rendering fidelity is not explicitly optimized.\n\nIn contrast, our optimization-based approach is data-efficient and directly minimizes rendering-space error via differentiable rendering, leading to better relighting consistency, though at the cost of slower optimization.\n\nAs shown in Table 1, our method outperforms CAMixerSR-FT across all PBR channels on tested artist-created meshes:\n\n- Albedo: 27.800 → 29.731  \n- Roughness: 30.642 → 31.602\n- Metallic: 28.961 → 31.889  \n- Normal: 25.655 → 29.088\n\nThese results confirm the advantage of rendering-aware supervision. Figure 3 further shows that while CAMixerSR-FT produces plausible textures, it often fails to preserve material fidelity under relighting, unlike our method.\n\nSimilarly, we additionally tested on 32 PBR meshes that are AI-generated, and our method still outperforms CAMixerSR-FT across all PBR channels:\n- Albedo: 31.439 → 33.821  \n- Roughness: 33.195 → 35.059\n- Metallic: 27.583 → 35.352 \n- Normal: 31.054 → 34.564\n\n\nWe emphasize that our work fills a current gap in this space by proposing an optimization framework that leverages 2D priors while ensuring high rendering fidelity, even without additional PBR training data. At the same time, we believe future work could explore hybrid strategies, such as:\n- Using learning-based models (e.g., CAMixerSR) for efficient initialization;\n- Incorporating rendering-aware loss functions into the training of feed-forward networks;\n- Jointly training on both texture-only and mesh-texture paired data, using our differentiable supervision as a guiding signal.\n\nWe have expanded our discussion accordingly in the revised manuscript."}]

Author rebuttal:

Meta-review: This submission presents a zero-shot framework for super-resolving PBR textures on 3D meshes by leveraging pretrained 2D image priors and iterative optimization with differentiable rendering. The reviewers unanimously recognized the paper's strengths, including its novel problem formulation, robust methodology, and high-quality empirical results. Key concerns raised during the review such as clarity of notation, evaluation scale, computational analysis, and robustness to input degradation, were thoroughly addressed by the authors in their rebuttal, with revisions and additional experiments (e.g., expanded datasets, failure case analysis, and runtime benchmarks) significantly improving the manuscript. The reviewers subsequently upgraded their ratings, reflecting confidence in the paper's technical soundness and impact. The submission is suggested for acceptance.
Decision: Accept (poster)