DeepMaterialInsights
A Web-based Framework Harnessing Deep Learning for Estimation, Visualization, and Export of Material Assets from Images
Saptarshi Neil Sinha (Fraunhofer Institute for Computer Graphics Research IGD)
Felix Gorsclüter (Fraunhofer Institute for Computer Graphics Research IGD)
Holger Graf (Fraunhofer Institute for Computer Graphics Research IGD)
M. Weinmann (TU Delft - Computer Graphics and Visualisation)
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Abstract
Accurately replicating the appearance of real-world materials in computer graphics is a complex task due to the intricate interactions between light, reflectance, and geometry. In this paper we address the challenges of material representation, acquisition, and editing by leveraging the potential of deep learning algorithms our framework provide. To enable the visualization and generation of material assets from single or multi-view images, allowing for the estimation of materials from real world objects. Additionally, a material asset exporter, enabling the export of materials in widely used formats and facilitating easy editing using common content creator tools. The proposed framework enables designers to effectively collaborate and seamlessly integrate deep learning-based material estimation models into their design pipelines using traditional content creation tools. An analysis of the performance and memory usage of material assets at various texture resolutions shows that our framework can be used plausibly according to the needs of the end-user.
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