ePBR: Extended PBR Materials in Image Synthesis
April 23, 2025 Β· Declared Dead Β· π 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Authors
Yu Guo, Zhiqiang Lao, Xiyun Song, Yubin Zhou, Zongfang Lin, Heather Yu
arXiv ID
2504.17062
Category
cs.GR: Graphics
Cross-listed
cs.CV
Citations
2
Venue
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Last Checked
4 months ago
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.
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