PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors (2506.02846v1)

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 view inconsistencies and lighting sensitivity, which is common in view-based super-resolution, our method applies 2D prior constraints across multi-view renderings, iteratively refining the 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.

• The paper introduces a zero-shot framework that refines low-res PBR textures using 2D image priors without additional training.
• It employs differentiable rendering and pixel-wise loss optimization to preserve spatial coherence across texture channels.
• Experimental results show significant PSNR improvements and enhanced rendering fidelity for applications in gaming and virtual reality.

Mesh PBR Texture Super Resolution from 2D Image Priors: An Analytical Review

The paper entitled "PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors" presents a novel approach to enhancing the resolution of physically-based rendering (PBR) texture maps from low-resolution inputs using 2D image super-resolution techniques. The method developed by Chen et al. leverages existing super-resolution models trained on natural images and employs an iterative optimization process to refine texture details directly within the PBR domain. This technique is executed in a zero-shot manner, implying no additional training data or model retraining is necessary.

Methodology and Core Contributions

The proposed approach addresses the challenge of enhancing PBR textures, which are crucial for rendering high-fidelity visual details in various applications including gaming and virtual reality. Unlike typical image super-resolution tasks, PBR texture super-resolution demands preserving spatial coherence across different texture channels—albedo, roughness, metallic, and normals—to ensure realistic material properties under dynamic lighting conditions.

The paper introduces a procedural framework that begins with an interpolation-based enhancement of the albedo texture and the application of a pretrained super-resolution model to establish a baseline for further refinement. Differentiable rendering is employed to synthesize multi-view images of a textured mesh, capturing rich surface detail across diverse views and lighting conditions. These renderings are then processed using a pretrained image restoration model that outputs high-resolution pseudo-ground truth images.

The optimization focuses on aligning differentiable rendered images with these pseudo-ground truths through a robust pixel-wise loss function and PBR consistency constraints. This iterative optimization is crucial for achieving high-fidelity PBR textures that can seamlessly integrate into existing rendering workflows while maintaining artistic intent.

Experimental Evaluation and Results

Extensive experiments demonstrate that the PBR-SR method significantly surpasses existing super-resolution models when applied directly to PBR textures. Quantitative evaluations, using PSNR across PBR maps and renderings, reveal marked improvements in texture fidelity and rendering quality, substantiating the effectiveness of the proposed zero-shot super-resolution framework. The paper reports superior results in preserving detail and realism, particularly in rendering evaluations which directly translate to practical applications such as relighting in high-quality 3D scenes.

Qualitative comparisons further support these findings, showing that PBR-SR overcomes the limitations of prior methods that fail to account for the physical nuance of PBR maps—issues often resulting in suboptimal material properties and spatial inconsistencies.

Implications and Future Directions

The implications of this research are twofold. Practically, it allows for the revival of low-resolution assets in applications demanding high visual fidelity, thus extending the lifespan and usability of existing resources without the need to recreate assets from scratch. Theoretically, the approach provides insights into leveraging natural image priors for domain-specific tasks, suggesting a potential avenue for further exploration using specialized priors in PBR domains.

Future developments inspired by this work may focus on combining multimodal inputs, like text or sketches, to complement texture super-resolution tasks when dealing with severely degraded inputs. Additionally, advancements in optimization speed and efficiency could render this technique applicable for real-time applications, fuelling innovation in interactive and immersive media technologies.

In conclusion, this paper presents a refined methodology for PBR texture super-resolution, efficiently bridging the gap between high fidelity requirements and existing low-resolution assets, thus contributing substantively to the advancement of rendering technology and 3D content generation.