ePBR: Extended PBR Materials in Image Synthesis (2504.17062v1)

Abstract: Realistic indoor or outdoor image synthesis is a core challenge in computer vision and graphics. The learning-based approach is easy to use but lacks physical consistency, while traditional Physically Based Rendering (PBR) offers high realism but is computationally expensive. Intrinsic image representation offers a well-balanced trade-off, decomposing images into fundamental components (intrinsic channels) such as geometry, materials, and illumination for controllable synthesis. However, existing PBR materials struggle with complex surface models, particularly high-specular and transparent surfaces. In this work, we extend intrinsic image representations to incorporate both reflection and transmission properties, enabling the synthesis of transparent materials such as glass and windows. We propose an explicit intrinsic compositing framework that provides deterministic, interpretable image synthesis. With the Extended PBR (ePBR) Materials, we can effectively edit the materials with precise controls.

ePBR: Extended PBR Materials in Image Synthesis

The paper "ePBR: Extended PBR Materials in Image Synthesis" presents advancements in the field of image synthesis, specifically targeting the intrinsic representation of images to facilitate the integration of transparent materials, such as glass and windows. As detailed by the authors, the intrinsic image representation approach balances realism and computational efficiency by decomposing images into intrinsic channels like geometry, materials, and illumination, which can be manipulated for controllable synthesis.

Overview

Traditional methods in image synthesis encompass three primary approaches: Physically Based Rendering (PBR), learning-based direct image generation, and intrinsic representation. PBR achieves high realism through simulating the physical interaction of light with geometry, materials, and illumination, but requires comprehensive 3D scene representation and is computationally demanding. Conversely, learning-based approaches, notably deep generative models such as diffusion models, synthesize images efficiently but lack control over physical consistency. This paper proposes a hybrid solution through intrinsic representation, which provides detailed control and efficient synthesis without expensive computational overhead, thus enabling manipulation in real-time environments.

The crux of their contribution is the extension of intrinsic channels to handle complex surface models, including those with high-specular and transparent properties, that traditional PBR materials struggle with. These extensions involve incorporating reflection and transmission attributes into intrinsic representations, effectively allowing the synthesis and manipulation of transparent materials. The explicit intrinsic compositing framework proposed by the authors emphasizes deterministic outputs, facilitating precise material editing.

Methodology

At the core of the ePBR materials, the authors introduce a comprehensive formulation combining reflection (using the Bidirectional Reflectance Distribution Function, BRDF) and transmission (using the Bidirectional Transmittance Distribution Function, BTDF) properties under the thin surface assumption. The approach revolves around the modification of the Disney Principled BSDF to incorporate transparency, providing a full BSDF model for both reflection and transmission. They employ a hybrid intrinsic compositing method that synthesizes images using intrinsic channels in screen space with deterministic, interpretable controls.

Results and Implications

Testing results highlight that the ePBR material approach yields superior image synthesis over prior methods, specifically in high-specular and transparent regions. The authors evaluate intrinsic channels both directly and through recomposed image synthesis and demonstrate that their method generates more accurate reflectance and transparency effects than those by diffusion-based models like RGB$\leftrightarrow$X.

This paper's contributions have substantial implications for virtual content creation, digital artistry, and real-time rendering applications. Its methodology can serve as an advanced framework for graphics pipelines that bridge traditional physically based rendering methods and modern AI-driven generative models. Future work proposed by the authors involves broadening the applicability of ePBR materials beyond 2D screen-space synthesis into more complex 3D vision applications, further advancing the convergence between physical accuracy and computational efficiency in digital environments.

Conclusions

The research encapsulated in this paper exemplifies an innovative extension of PBR materials to accommodate transparency in intrinsic image synthesis, achieved through an explicit, deterministic compositing framework. Such developments hold promise for various applications requiring real-time flexibility and precision in material editing, offering new pathways for deploying AI-driven methodologies alongside conventional rendering techniques in sophisticated visual tasks. The methodological improvements discussed here pave the way for more nuanced and controllable image synthesis, promising significant advancements in the field of computer graphics and vision.