Ph.D. Researcher @ TU Delft
Tzu-Hsin Hsieh
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Spatial AI · Generative 3D · XR Systems
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Spatial AI · Generative 3D · XR Systems
I am a Ph.D. researcher at the Computer Graphics and Visualization Group, TU Delft, specializing in spatial AI, generative 3D, and XR systems.
My work focuses on building end-to-end research prototypes that combine VLM-based 3D scene understanding, generative AI, computer vision, XR hand tracking, and human-centered XR interfaces for spatial reasoning and content generation.
With an interdisciplinary background in Architecture, Computer Science, and Electrical Engineering, I design systems that bridge high-level spatial intelligence with real-time human interaction in physical and virtual environments.
Conducting research on spatial AI, generative 3D, and 3D scene understanding for XR-based spatial reasoning and content generation. Developed XR prototypes with hand tracking, real-time interaction logging, adaptive feedback control, and user-study evaluation.
Built an AR iOS application using real-world weather data, AprilTag tracking, and plane detection for spatial model registration.
Conducted computer vision research on AI-based image recognition, super-resolution, satellite-image analysis, and deep learning for remote sensing applications.
Developed mixed reality and digital-twin systems integrating BIM, 3D spatial data, and collaborative interaction workflows.
VLM-based 3D scene understanding, spatial grounding, and multimodal reasoning for intelligent spatial interaction.
Fit-aware 3D object synthesis and generative adaptation for context-aware spatial content generation.
Human-centered XR interfaces with hand tracking, adaptive feedback, and real-time interaction for spatial tasks.
Designing AI systems with user studies, adaptive interfaces, and evaluation frameworks for real-world deployment.
Designed and evaluated a VR piano learning system with adaptive ghost-hand guidance. Compared static vs. skill-adaptive conditions through a user study measuring retention, over-reliance, and workload.
Built a collaborative multi-user MR environment where users interact with a shared digital twin in real time, integrating BIM and 3D spatial data via HoloLens 2.
Proposed a multi-stakeholder recommendation system leveraging attention-graph Q-learning with multi-scale spatial heterogeneity for transportation and green attraction planning.
Proposed a GIS-based AR system combined with Cluster-GCN recommendation to improve public transport access to green attractions, integrating spatial graph learning with real-world transit data.
Proposed a lightweight multi-path CNN architecture for image super-resolution optimized for efficiency under hardware constraints.
Developed a predictive modeling system for urban pedestrian flow monitoring integrating diverse geospatial datasets for smart city applications.
Fit-aware 3D object insertion via VLM reasoning and generative adaptation. Combines semantic-spatial grounding, scene-conditioned object generation, and physics-aware fitting with rigid, uniform, and elastic adaptation modes.
VR piano learning system with ghost-hand guidance, real-time hand tracking, and skill-adaptive transparency control. Evaluated retention, over-reliance, and cognitive workload through a formal user study.
Collaborative multi-user MR space with real-time digital twin interaction, BIM integration, and shared spatial workflows for urban design visualization.
AR app visualizing real-time 3D weather data from Taiwan's Central Weather Bureau onto a physical island model using AprilTag-based spatial registration.
Pedestrian flow prediction dashboard built with Tainan City Government and Far EasTone, using GRU-based time-series modeling and geospatial data analysis.
VR environment using connected virtual spaces and scale mismatches to create impossible-space and size-perception effects — exploring spatial cognition in XR.
Open to research collaborations, internship opportunities, and conversations about Spatial AI & XR.