3D (3-dimensional) Image Synthesis (3DIS) is a technology to render objects from different views which enables numerous applications in computer graphics and computer vision. As the digital world is becoming more crucial especially in the times of pandemic, 3DIS can provide tools for online classes, virtual social tours, improved gaming experience and simulators for robotics by providing realistic virtual 3D environments. Furthermore, 3DIS by disentangling the attributes of objects and entangling them via a renderer for synthesize, can provide a technology to learn useful features from our visual world that can be used for video understanding, one of the biggest goals of artificial intelligence. Here, I propose 3DIS-NN, a set of methods to improve the quality of 3DIS with deep neural networks (DNNs), and bring it close to the production quality, which will contribute to the European Union’s Future and Emerging Technology ambitions of Horizon Europe. Learning 3DIS from 2D images with deep learning is a challenging topic due to its inherent ambiguity. 3DIS-NN will enable high-quality 3DIS results by i) creating a dataset with weak labels to feed the data-hungry DNNs for better accuracy, ii) improving robustness of 3D geometry and texture prediction from images, iii) handling the impurities in segmentation of objects with a novel design of architecture, and iv) providing a tool to further close the domain gap in renderers and real images. This interdisciplinary proposal which is at the intersection of deep learning and computer graphics will be carried out at under the supervision of Prof. Ugur Gudukbay who is an expert in computer graphics. In terms of career developments, this proposal will consolidate and accelerate my career on the international landscape scene as a pioneer lead authority in the new cross-disciplinary area of “deep learning & computer graphics”.

Membranes offer effective solutions for a wide range of separation processes, such as desalination, water treatment, air filtering, biomolecular detection and gas separation. Despite their effectiveness versus other separation methods, the conventional membrane concept is based on either long and tortuous pores, or solution-diffusion, both limiting the permeation rates and causing fouling. A new paradigm to overcome this limit is to use atomically-thin pores, which do not exert any hindering force during permeation, yielding ballistic mass transport. Recent advances in graphene technology enabled the realization of this new concept, and indeed, our recent work has demonstrated ballistic gas transport through graphene pores covering a sub-mm area (Science 344 (6181) 289, (2014)). In this proposal, we focus on this atomically-thin membrane concept, and aim to: (1) develop methods to obtain cm-scale, fiber-frame-supported graphene membrane with sub-10-nm pores, achieving several orders of magnitude faster permeation compared to the best gas separation membranes; and (2) narrow-down the graphene pore diameter to sub-2-nm, and thus demonstrate ballistic molecular sieving for the first time. This project will be a key step to develop the next generation industrial membranes, replacing polymers and other conventional materials by graphene, thus promising significant economic impact. Meanwhile, scientifically, the nanoporous platforms obtained here can also enable the study of nanoscale mass transport phenomena, quantum nanofluidics, and biomolecular sorting and detection.