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About

Hong-Xing Yu is a leading researcher in computer vision and graphics, specializing in neural scene representations and photorealistic dataset generation. His most impactful contribution is Neural Radiance Flow (NeRFlow), a groundbreaking method for 4D view synthesis and video processing that learns spatial-temporal representations of dynamic scenes from RGB images. This work, cited over 200 times, enables novel view synthesis of moving scenes by capturing 3D occupancy, radiance, and dynamics through neural implicit representations. Yu also pioneered the OpenRooms framework, which transforms 3D scans into large-scale photorealistic indoor scene datasets with ground truth geometry, material, lighting, and semantics. This open-source framework, with nearly 80 combined citations, democratizes high-quality dataset creation for indoor scene understanding. His work bridges the gap between static and dynamic scene modeling, advancing applications in virtual reality, video processing, and embodied AI. Yu’s research has been recognized for its practical impact on making complex scene representations accessible to the broader research community.