MBZUAI ·
A researcher at the University of Oxford presented new findings on 3D neural reconstruction. The talk introduced a dataset comprising real-world video captures with perfect 3D models. A novel joint optimization method refines camera poses during the reconstruction process. Why it matters: High-quality 3D reconstruction has broad applicability to robotics and computer vision applications in the region.
MBZUAI ·
Egor Zakharov from ETH Zurich AIT lab will present research on creating controllable and detailed 3D head avatars using data from consumer-grade devices. The presentation will cover high-fidelity image-based facial reconstruction/animation and video-based reconstruction of detailed structures like hairstyles. He will showcase integrating human-centric assets into virtual environments for real-time telepresence and entertainment. Why it matters: This research contributes to advancements in digital human modeling and telepresence, with applications in communication and gaming within the region.
KAUST ·
The KAUST Visual Computing (KAUST RC-VC) – Modeling and Reconstruction conference featured speakers from Simon Fraser University, Caltech, Cornell University, and Autodesk. Presentations covered topics like networking topology, shape matching and modeling, data-driven interpolation of optical properties, and computer graphics. Why it matters: The conference highlights KAUST's role in fostering international collaboration and advancing research in visual computing and related fields within Saudi Arabia.
KAUST ·
Dr. Laurent A. Lantieri delivered a keynote address at KAUST on April 17, 2017, discussing microsurgical procedures. The address included a brief history of microsurgery. The event took place in the University Auditorium. Why it matters: Such events expose the KAUST community to advances in specialized medical fields and potential research applications.
TII ·
Conor McMenamin from Universitat Pompeu Fabra presented a seminar on State Machine Replication (SMR) without honest participants. The talk covered the limitations of current SMR protocols and introduced the ByRa model, a framework for player characterization free of honest participants. He then described FAIRSICAL, a sandbox SMR protocol, and discussed how the ideas could be extended to real-world protocols, with a focus on blockchains and cryptocurrencies. Why it matters: This research on SMR protocols and their incentive compatibility could lead to more robust and secure blockchain technologies in the region.
MBZUAI ·
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.
MBZUAI ·
This article discusses a talk by Gábor Lugosi on "network archaeology," specifically the problems of root finding and broadcasting in large networks. The talk addresses discovering the past of dynamically growing networks when only a present-day snapshot is observed. Lugosi's research interests include machine learning theory, nonparametric statistics, and random structures. Why it matters: Understanding the evolution and origins of networks is crucial for various applications, including analyzing social networks, biological systems, and the spread of information.
MBZUAI ·
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.