Title: PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors
Venue: NeurIPS 2025
Accepted: yes
Keywords: Mesh Texture Super Resolution
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

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-theshelf 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 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.

1 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 in 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 or 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 datasets exist for supervised SR tasks [5, 6, 23], 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.

Existing image SR models can enhance PBR textures by processing every three channels separately. However, current state-of-the-art 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 lighting-dependent inconsistencies, since these models do not disentangle material properties from appearance. To tackle these challenges, we directly optimize PBR textures with differentiable rendering to a 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 a 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 pseudo-ground truth (GT) images. We optimize the SR PBR textures by minimizing the discrepancy between differentiable renderings and pseudo-GTs. Leveraging the viewindependence 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 view-aware optimization. Extensive experiments validate that our method achieves state-of-the-art performance in both texture fidelity and rendering quality, facilitating applications such as 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 in UV texture space (for initialization) and in the 2D rendering space (for iterative refinement).

Albedo

Normal

Metallic+

Roughness

Low-res PBR Texture

Albedo

Normal

Metallic+

Roughness

Super-res PBR Texture

Super-res

Initialization

PBR Loss

Low-res Renderings

Super-res Renderings

Diff. Renderings

Multi-view

Rasterize

Robust

Pixel-wise Loss

Input

Trainable

Non-Trainable

Loss

Forward

Backward

Update

Iteratively update

Mesh

Encoder &

add noise

multiple steps

Image Restoration

Latent Diffusion

Decoder

Weighting Maps

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 a 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) [13, 28]. While PBR has found wide adoption in artist workflows and rendering engines [8, 12, 18], the creation or remastering of spatially varying PBR materials with textures remains a difficult and time-consuming task in which SR methods tailored to materials can help.

Image Restoration and Super Resolution. Recent advances in image restoration and superresolution (SR) have significantly enhanced image quality across various applications [20, 22, 40, 42, 26]. Notable transformer-based methods, including CAMixerSR [34], HiT-SR [39], and HAT [10], improve reconstruction through hierarchical feature fusion and adaptive feature mixing. MambaIR [15] employs state-space models to address complex spatial relationships effectively. Diffusion-based approaches such as DiffBIR [24] and StableSR [33] excel in generating rich, high-frequency details but typically lack multi-view consistency. Recent works explore multi-view image SR with NeRFbased representations[17, 32, 43] or 3D 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 to achieve realistic results [9, 11, 16, 25, 30, 36, 37, 41]. Texurify [31] uses a GAN-based method to generate textures directly on the mesh surface, ensuring consistency across views, while Convolutional Generation of Textured 3D Meshes [27] learns to generate both meshes and textures using 2D supervision. Diffusion models have also proven effective, as seen in Text2Tex [9], 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

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 an SR model on a 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, Decorate3D [16] uses a standard SR model to upscale generated 512 × 512 UV textures to 2048 × 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.

3 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-quality renderings. We denote the input LR and the output SR PBR texture maps by albedo { T d lr , T d sr }, roughness { T r lr , T r sr }, metallic { T m lr , T m sr } and surface normal { T n lr , T n sr }, 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 a pretrained SR model to generate pseudo-GTs. A differentiable renderer synthesizes images from the same viewpoints using the current HR textures. By iteratively optimizing the textures to 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 d sr . For the ARM maps (including ambient occlusion (AO), roughness T r , and metallic T m ) as well as the normal map T n , we apply bicubic interpolation to upsample them to the target resolution. If AO is unavailable, an empty map is allocated in the red channel as a 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 p on the mesh surface, the albedo a θ ∈ R 3 , roughness α θ ∈ R , metallic m θ ∈ R , and normal direction n θ ∈ R 3 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 F 0 ,θ ∈ R 3 is calculated as:

The rendered color L θ ( p , ω ) at surface point p , viewed from direction ω , is computed using the rendering equation:

where ω i is the incident light direction, Ω is the hemisphere around the surface normal n θ , and L i is the incident light from the environment map. The BRDF f θ ( p , ω i , ω ) models the material properties, including albedo a θ , specular F 0 ,θ , and normal n θ .

This rendering equation can be decomposed into a diffuse term D θ ( p ) and a specular term S θ ( p , ω ) using the Cook-Torrance microfacet model [13]:

where D θ , F θ , and G θ represent the microfacet distribution, Fresnel term, and geometric attenuation, respectively. D θ and G θ depend on roughness α θ , and F θ is based on specularity F 0 ,θ .

Once all surface points are processed, the rendered image I θ is generated from a specific camera. For brevity, we denote the rendering process as I θ = R M ( T d sr , T r sr , T m sr , T n θ ) , where R M ( · ) is the differentiable mesh PBR rendering function.

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 I θ with a 4 × super-resolution scaling using DiffBIR [24], a unified blind image restoration framework based on diffusion models. DiffBIR consists of two cascaded stages: first, it removes image degradations to yield intermediate high-fidelity restorations; second, it leverages a specially designed IRControlNet module built on latent diffusion models to regenerate realistic high-frequency details. Specifically, we 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 SR θ 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 d sr , T r sr , T m sr , T n sr } 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, we normalize the mesh and position cameras on a surrounding sphere to generate a 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 a batch of b viewpoints in the first iteration. For each view, we 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-GT I SR θ .

Simultaneously, at the same view, we render an image I HR by differentiable rendering in Sec. 3.3 with the same resolution of the pseudo-GT I SR θ using the optimizable PBR texture map { T d sr , T r sr , T m sr , T n sr }. The loss function to 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, we propose a robust pixel-wise optimization scheme that downweights unreliable pixels via a learnable per-image pixel weighting map W i ( u, v ) ∈ [0 , 1] , where ( u, v ) denotes a pixel location and i indexes the image. We denote the predicted PBR rendering as I SR i ∈ R C × H × W and the target as I HR i . The robust pixel-wise loss is defined as:

This formulation computes a normalized weighted MSE per image and then averages across the batch. Importantly, squaring W i ( u, v ) 2 allows sharper modulation of unreliable regions. To regularize the

learned weight maps, we penalize deviation from 1 using a per-pixel mean squared penalty:

The final robust rendering loss becomes:

where λ pix is a global scaling factor and λ reg 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 to stabilize PBR learning under noisy pseudo-ground-truth supervision.

3.5.2 PBR Constraints

PBR Consistency Loss: This term ensures consistency between the optimized HR PBR texture maps and the LR texture input 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:

where T θ denotes the current HR PBR texture map being optimized; T lr indicates the corresponding LR textures, and w T 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 to match the LR input. L ssim is a SSIM loss and λ ssim is a weighting factor.

PBR Total Variation (TV) Regularization: To encourage spatial smoothness and suppress undesirable artifacts in the optimized PBR textures, we introduce a total variation loss:

where ∇ x and ∇ y 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:

where λ pbr and λ tv are weighting factors to balance these three loss terms.

4 Results

In this section, we present the experimental setup, evaluation metrics, and results obtained from our proposed method for PBR texture super resolution.

4.1 Experimental Setup

Datasets. Since there is no established benchmark on mesh PBR texture super resolution, we collect a set of PBR meshes with rich texture information [1, 2, 4] ( Artist-designed Dataset ). This collection contains 16 different high-quality meshes with high-resolution PBR maps in 4096 × 4096 or 8192 × 8192 resolutions, and their low-resolution counterparts with a 4 × smaller resolution. In addition, we evaluate on 32 AI-generated PBR-textured meshes sourced from the Hyper3D commercial platform [3] ( AI-generated Dataset ). These meshes contain 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. Each model contains a 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 as ground truth for performance evaluation.

Pretrained SR Models. For albedo initialization and generating pseudo-GT (Sec . 3.4, Sec . 3.5), we utilize DiffBIR[24], a 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 [19] to produce rendered images from selected viewpoints. These images are then super-resolved using the same image SR model to create pseudo-GTs for optimization. Unless explicitly specified, the same environment lighting setup is used for both optimization and evaluation. The optimization uses the Adam optimizer with a constant learning rate of 1 × 10 -4 . In each iteration, we use a batch size of 4, which corresponds to 4 viewpoints. We stop the iterative optimization after 2000 iterations.

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 [10], CAMixerSR [34], OSEDiff [35], StableSR [33] and DiffBIR [24], which are applied directly on the PBR texture maps.

CAMixerSR-FT : Since CAMixerSR achieves overall balanced performance on all PBR channels, we use its architecture and pretrained weights from natural images, and then fine-tune it on a collection of 24,000 LR-HR PBR map pairs (120 × 120 to 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 a supervised baseline.

Paint-it SR : Paint-it [36] is a model designed for text-driven mesh PBR texture generation. We adopt a variant of Paint-it as an optimization-based baseline for the PBR texture SR task by replacing its text-to-image diffusion model and SDS loss with an image super-resolution model and a render loss function.

Evaluation Metrics. We evaluate the performance of our PBR texture SR method in 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) of SR results with 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.

Quantitative Evaluation. Table 1 presents 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-based 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 CAMixerSR-FT, a 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 on the normal map

HAT

Paint-it SR

CAMixerSR-FT

CAMixerSR

DiffBIR

OSEDiff

Ours

GT

LR

HAT

Paint-it SR

CAMixerSR-FT

CAMixerSR

DiffBIR

OSEDiff

Ours

GT

LR

HAT

Paint-it SR

CAMixerSR-FT

CAMixerSR

DiffBIR

OSEDiff

Ours

GT

LR

but falls short on others, especially in rendering quality. Transformer-based 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 in high-quality material and renderings. In Table 2, we provide quantitative results comparing our method against the two strongest baselines. The trends observed on the 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.

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 , the Cradle Globe , and the Chinese Chandelier at 4 × 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 or reflection artifacts. OSEDiff, which employs a diffusion model, produces high-frequency details, though it often diverges from the LR cues, erasing or altering LR

LR

OSEDiff

DiffBIR

Paint-it SR

Ours

GT

Albedo

ARM map

Normal map

details, with material property shifts causing incorrect brightness and shading. DiffBIR introduces too much noise and struggles to 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 a lack of sharpness in the rendered appearance, as the model is not explicitly optimized for rendering fidelity. Paint-it SR, an optimization-based model, iteratively improves PBR renderings but appears more blurred than ours. Our method achieves results that are closest to the GT renderings.

Qualitative Comparison on PBR Texture Maps. Fig. 4 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, DiffBIR, Paint-it SR, and Our method PBR-SR, GT HR PBR texture. As shown in the figure, our method significantly outperforms all inferenceand optimization-based baselines. Especially in comparison with the LR PBR texture, our method demonstrates a substantial improvement in texture quality, highlighting the effectiveness of applying image SR priors to the task of PBR texture SR.

Ablation Study. Table 3 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 on Image SR Models in PBR-SR. In Table 4, 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. DiffBIR 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 a 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, DiffBIR is computationally more efficient than StableSR, offering faster inference while maintaining high-quality outputs. This makes it 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 PBR-specific priors with multimodal integration.

For additional details and results, please refer to the Supplementary Materials.

5 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-the-art image SR models and optimization-based 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

Cgtrader models. https://www.cgtrader.com .

The gltf v2.0 sample models. https://github.com/KhronosGroup/glTF-Sample-Assets .

Hyper3d models. https://hyper3d.ai/ .

Poly haven models. https://polyhaven.com/models .

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.

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.

James F. Blinn. Models of light reflection for computer synthesized pictures. In SIGGRAPH , pages 192-198. ACM, 1977.

Brent Burley and Walt Disney Animation Studios. Physically-based shading at disney. In SIGGRAPH , pages 1-7. ACM, 2012.

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 Conference on Computer Vision , pages 18558-18568, 2023.

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:2309.05239 , 2023.

Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl, Michael Paulitsch, Matthias 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.

Blender Online Community. Blender - a 3D modelling and rendering package . Blender Foundation, Stichting Blender Foundation, Amsterdam, 2018.

Robert L Cook and Kenneth E. Torrance. A reflectance model for computer graphics. ACM Transactions on Graphics (ToG) , 1(1):7-24, 1982.

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.

Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. Mambair: A simple baseline for image restoration with state-space model. arXiv preprint arXiv:2402.15648 , 2024.

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 Thirty-seventh Conference on Neural Information Processing Systems , 2023.

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.

Brian Karis. Real shading in Unreal Engine 4. In SIGGRAPH Physically Based Shading in Theory and Practice course , 2013.

Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics , 39(6), 2020.

Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic single image superresolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4681-4690, 2017.

Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, and Luc Van Gool. 3d appearance superresolution with deep learning. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 9671-9680, 2019.

Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc 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.

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.

Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Bo Dai, Fanghua Yu, Yu Qiao, Wanli Ouyang, and Chao Dong. Diffbir: Toward blind image restoration with generative diffusion prior. In European Conference on Computer Vision , pages 430-448. Springer, 2024.

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-guided reconstruction model. arXiv preprint arXiv:2408.10198 , 2024.

Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön. Controlling visionlanguage models for universal image restoration. arXiv preprint arXiv:2310.01018 , 3(8), 2023.

Dario Pavllo, Jonas Kohler, Thomas Hofmann, and Aurelien Lucchi. Learning generative models of textured 3d meshes from real-world images. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 13879-13889, 2021.

Matt Pharr and Greg Humphreys. Physically Based Rendering: From Theory to Implementation (The Interactive 3d Technology Series) . Morgan Kaufmann, August 2004.

Bui Tuong Phong. Illumination for computer generated pictures. Commun. ACM , 18(6):311-317, 1975.

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-to-mesh generation with high-quality geometry, texture, and pbr materials. arXiv preprint arXiv:2407.02445 , 2024.

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. Springer, 2022.

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.

Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C.K. Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. 2024.

Yan Wang, Yi Liu, Shijie Zhao, Junlin Li, and Li Zhang. Camixersr: Only details need more 'attention'. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 25837-25846, June 2024.

Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world image super-resolution. arXiv preprint arXiv:2406.08177 , 2024.

Kim Youwang, Tae-Hyun Oh, and Gerard Pons-Moll. Paint-it: Text-to-texture synthesis via deep convolutional texture map optimization and physically-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4347-4356, 2024.

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.

Xiqian Yu, Hanxin Zhu, Tianyu He, and Zhibo Chen. Gaussiansr: 3d gaussian super-resolution with 2d diffusion priors. arXiv preprint arXiv:2406.10111 , 2024.

Xiang Zhang, Yulun Zhang, and Fisher Yu. Hit-sr: Hierarchical transformer for efficient image superresolution. arXiv preprint arXiv:2407.05878 , 2024.

Yulun Zhang, Kunpeng 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.

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) , 43(4):1-18, 2024.

Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, and Wei-Shi Zheng. Selective hourglass mapping for universal image restoration based on diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 25445-25455, 2024.

Kun Zhou, Wenbo Li, Yi Wang, Tao Hu, Nianjuan Jiang, Xiaoguang Han, and Jiangbo Lu. Nerflix: High-quality neural view synthesis by learning a degradation-driven inter-viewpoint mixer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 12363-12374, 2023.

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 [1, 2, 4]. To better evaluate PBR texture super-resolution performance, we select meshes that contain rich and diverse textures in different PBR channels. Table 5 lists 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 a 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 is not redistributed here. We used the dataset solely for method evaluation. No training, fine-tuning, or derivative model development was performed using this data.

AI-generated Dataset. To strengthen the evaluation, we add 32 additional meshes from the commercial Hyper3D website [3], which features AI-generated PBR textures with diverse content. Table 6 lists the meshes and their corresponding website links. Each sample includes PBR maps at 512 × 512 resolution as input and 2048 × 2048 resolution as ground truth (GT). We perform 4 × super-resolution as described in the main paper, keeping all experimental settings consistent with those used for the Artist-Designed 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 a 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 λ pix is set to 100 and λ reg is 0.5 in the robust pixel-wise loss term. The weighting map is optimized in resolution of 64 × 64 and is interpolated to W i with the same resolution as the rendering's image when calculating L pix. 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 w K d , roughness w K r , and normal map w K n , and 0.1 for the metallic map w K m . The SSIM part is weighted by λ ssim = 10 for all. The overall PBR consistency loss is given a weight λ pbr = 10 and the PBR TV loss is weighted by λ tv = 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 1K-to-2K

resolution offline on average. The optimizable parameters are limited to the high-resolution PBR textures, as we directly update them using computed gradients.

A.3 Baselines

We leverage the official implementations of SwinIR [22], HAT [10], CAMixerSR [34], 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, if 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 it to suit the super-resolution (SR) task. Specifically, 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. The deep convolutional block is reinitialized 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 low-frequency information already captured in the LR inputs. Although Paint-it SR shares a similar optimization framework with the original method, it is explicitly re-purposed for SR rather than text-to-texture synthesis. These modifications make it a strong baseline, yet our proposed approach surpasses it through a more effective integration of SR priors and view-consistent optimization.

For CAMixerSR-FT, we make it a supervised baseline using PBR textures to fine-tune the off-the-shelf CAMixerSR weights pre-trained on natural images. We use 19 high-quality 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 a resolution of 4096 × 4096 . For training data generation, all texture maps are downsampled to 1024 × 1024 using bicubic interpolation. From these, we randomly extract 480 × 480 patches as high-resolution targets and their corresponding 120 × 120

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 × 4 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 AdamW optimizer with a learning rate of 1 × 10 -4 , weight decay of 1 × 10 -5 , and ( β 1 , β 2 ) = (0 . 9 , 0 . 99) . A multi-step learning rate scheduler is used with decay milestones at 20K and 40K iterations and a 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 is 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 is 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 as view inconsistency, lighting variation, and shadow misalignment. To validate the necessity of this design, we conduct an ablation by replacing our robust loss with a standard pixel-wise rendering loss: L pix = 1 b ∑ b i =1 ∥ ∥ I SR θ -I HR θ ∥ ∥ 2 2 , where b denotes the number of rendered views and θ represents the optimizable parameters (i.e., 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 i ( u, v ) ∈ [0 , 1] that adaptively downweights uncertain pixels. We regularize these weights toward one to ensure stable optimization: L robust = λ pix · L pix + λ reg · R ( W ) . This design improves both the stability and quality of optimization, particularly in regions prone to multi-view inconsistencies. As shown in Table 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. 5 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 a uniform pixel-wise average over multi-view supervision, which often introduces blocky artifacts. This issue is exacerbated by the PBR consistency loss, which operates in 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 in cleaner and more physically plausible texture reconstructions.

LR

w/o robust

Ours

Untextured Mesh

Generated PBR Texture

PBR-SR Texture

LR

Ours

LR

Ours

Ablation on Rendering Resolution in Optimization. We assess the impact of rendering resolution on optimization performance using the Table Clock model at 128 × 128 , 256 × 256 , 512 × 512 , 768 × 768 , and 1024 × 1024 . As shown in Table 8, increasing the resolution of the pseudo-ground 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 to 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 × 1024 and the significantly increased computational and memory costs at higher resolutions, we adopt 1024 × 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 3 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 9. We observe that performance improves steadily from 8 to 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.

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 10. Bicubic interpolation for metallic and roughness slightly outperforms nearest-neighbor interpolation, providing smoother transitions that facilitate optimization-even 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-interpolation normalization amplifies these artifacts and degrades quality. Overall, bicubic interpolation serves as a robust and effective initialization strategy, while any minor inconsistencies are further corrected through our optimization process.

Robustness Analysis on Degraded LR Textures. To evaluate the robustness of our optimizationbased 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 σ = { 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, we report both the final PSNR after optimization and the PSNR improvement ( ∆ ) relative to initialization, for the albedo map and the rendered appearance (averaged over views). Results are summarized in Table 11. Our method consistently improves both albedo and rendering quality, even under moderate or severe degradation. Performance gradually decreases as 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 × 1024 PBR textures from an untextured mesh ( Damaged Helmet ), and apply PBR-SR to perform 4 × super-resolution on the generated PBR maps. As shown in Fig. 6 (right), our method successfully introduces highfrequency 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 generated 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.

Qualitative Results on Composed Scenes. To evaluate PBR-SR on scenes with multiple objects, we composed eight meshes from the CGTrader dataset. Figure 7 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-based Methods for PBR Texture SR. While the learning-based baseline (CAMixerSR finetuned on our PBR dataset) achieves moderate improvements, its performance is limited by the scarcity of large-scale, diverse PBR texture-mesh datasets and by image-space loss functions that fail to capture physically based effects such as relighting consistency and material fidelity. In contrast, our optimization-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 as complementary: learning-based models provide efficient initialization and inference, whereas optimization-based refinement ensures physically grounded fidelity. Future research could explore hybrid frameworks that integrate differentiable rendering losses and PBR-specific regularization into the training of feed-forward networks, combining the strengths of both approaches for high-quality, physically consistent PBR texture super resolution.

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 to suboptimal outputs.

Video. We include a supplementary video 1 showcasing 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.

Rendering with LR PBR Texture

Rendering with Our texture

Lighting: De Balie

Lighting: Beach Parking

LR PBR Rendering

PBR-SR Rendering

LR PBR Rendering

PBR-SR Rendering

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nPlease 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\nPlease refer to weaknesses for more details.\n\nLIMITATIONS:\nWhile 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nI 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}