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.
arXiv ·
The paper introduces OmniGen, a unified framework for generating aligned multimodal sensor data for autonomous driving using a shared Bird's Eye View (BEV) space. It uses a novel generalizable multimodal reconstruction method (UAE) to jointly decode LiDAR and multi-view camera data through volume rendering. The framework incorporates a Diffusion Transformer (DiT) with a ControlNet branch to enable controllable multimodal sensor generation, demonstrating good performance and multimodal consistency.
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 ·
Marc Pollefeys from ETH Zurich and Microsoft Spatial AI Lab will discuss building 3D environment representations for assisting humans and robots. The talk covers visual 3D mapping, localization, spatial data access, and navigation using geometry and learning-based methods. It also explores building rich 3D semantic representations for scene interaction via open vocabulary queries leveraging foundation models. Why it matters: Advancements in spatial AI and 3D scene understanding are critical for enabling more capable robots and AI assistants in various applications within 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.
MBZUAI ·
Ekaterina Radionova from Smarter AI (formerly Samsung AI Center) presented an approach to generating lifelike real-time avatars. The work focuses on generating high-quality video with authentic facial features to support online generation. Radionova's master's degree is from Skoltech on Data Science program and Bachelor degree at Moscow Institute of Physics and Technology on Applied Math. Why it matters: Achieving realistic real-time avatars is critical for applications in online communication, entertainment, and virtual reality within the region.