Realistic image synthesis techniques from computer graphics enable the use of simulation in a wide variety of important fields including architecture, industrial design and communication, military, medical, and emergency training, cultural heritage preservation, film production, and gaming. Realistic modeling of material appearance is an essential component of the image synthesis process. Current approaches to material modeling include analytical modeling, numerical simulation, and image-based capture. Each approach has distinct advantages and limitations, and different ranges of applicability. The lack of unity makes material modeling difficult and has limited the useful application of computer graphics image synthesis. This transformative research will change the way materials are modeled in computer graphics systems. Rather than using disparate models as at present, this project will unify these approaches into a common physical and perceptual framework that will serve as the basis for a rich set of tools for material modeling that are physically accurate, phenomenologically expressive, computationally efficient, and easy to use. This work should enable the use of computer-aided material design methods in a wide range of economically and culturally important applications. Creating this framework will involve three subprojects.
Development of a material simulation testbed: In this subproject a suite of tools for material simulation will be developed that includes both Monte Carlo and deterministic algorithms. Different classes of materials (paints, metals, textiles) will be modeled, and different numerical methods will be tested and compared. The resulting simulation tools and a database of the simulated materials will be distributed.
Unification of analytical, simulation, and image-based capture material modeling methods: In this subproject the analytical models that represent general classes of materials will be unified with simulation and image-based capture data that represent specific material instances. In the first part of this subproject simulation and capture data will be fit with a range of analytical models, considering both individual materials and "families" of materials generated by progressively changing the parameters of the simulation models. In the second part of this project methods for inferring the microstructures of materials measured using image-based capture methods will be developed. The approach will be to identify the class of a material and then vary the parameters of an appropriate simulation model to best reproduce the captured data. The results of this subproject will be expressive and efficient analytical material models that are physically grounded because they are based on captured data and rigorous simulations.
Development of perceptually-based material design tools: An important criterion for material modeling is usability. Material designers need to be able to easily specify and visualize material appearance properties. This requires consideration of the human factors in material modeling. In this subproject a series of psychophysical experiments on material perception will be conducted and the results will be used to derive perceptually-based material models with meaningful parameters. How image properties affect the visual fidelity of rendered materials will also be investigated. These findings will then be used to develop effective and easy-to-use interfaces for computer-aided material design.
Broader Impacts: Better methods for material modeling and rendering will lead to improved capability and productivity in fields such as architecture, industrial design and communication, training, cultural heritage, and entertainment. The project will build a material appearance community that stretches across academic and commercial boundaries to include computer graphics, computer vision and human vision researchers along with a range of industrial collaborators, and which focuses on developing effective solutions to real-world problems. The research will engage and train groups of students at 3 universities for scientific/technical careers that require working in interdisciplinary teams and partnering with coworkers in remote locations.
