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Re-storying Fort Ancient
Re-storying Fort Ancient: An AI-Augmented Digital Twin Installation for Indigenous Earthwork
PI: Ming Tang, Co-Is: John Hancock, Tianyu Jiang, Samira Sarabandikachyani. $5,000. CDRI Grant, DAAP, UC. 5/23/2026- 8/23/2026
Students: Saptarshi Ghosh, Mikhail Nikolaenko, Johnny Bozhi Peng, Ahmad Alrefai
This project proposes the development of an AI-augmented Digital Twin installation for the Fort Ancient Earthworks, a UNESCO World Heritage site in southern Ohio. Using reality capture, spatial computing, and Large Language Models (LLMs), the project will create an interactive web- and mobile-based experience that allows users to explore the site through immersive visualization and natural language conversation.
The project builds upon existing 3D earthwork models previously developed by CERHAS together with earlier XR-Lab spatial computing workflows. This foundation allows the team to focus on AI-driven interaction, interpretation, and public engagement without needing to reconstruct the site from the ground up. The AI system will be developed through a carefully curated Retrieval-Augmented Generation (RAG) workflow using approved scholarly, archival, and Indigenous-informed materials as its knowledge base.
LiDAR Scan of the Hut
Historic Avondale in VR
Virtual Reality Development for Historic Avondale and Digital Heritage
Principal Investigator (PI): Anne Delano Steinert, Co-PI: Ming Tang. ($14,000 allocation to DAAP)
Society and Culture Grant, UC Research Office. 2026. University of Cincinnati. Timeline: 1/13/2026-07/01/2026
Project Overview
The Historic Avondale Digital Heritage Project aims to create an immersive Virtual Reality (VR) reconstruction of Cincinnati’s historic Avondale neighborhood, focusing on cultural heritage preservation and community engagement. Through a collaborative effort between A&S and DAAP’s XR-Lab, the project will visualize approximately six urban blocks of Avondale as they appeared in a historically significant period in 1950s, enabling users to explore the environment through VR headsets and interact with its digital elements.
This initiative supports experiential learning, public history dissemination, and digital preservation. The final VR experience will be designed for accessibility on standalone VR headsets (Meta Quest) and optimized for community outreach, classroom education, and museum-style exhibition.
ARCH Seminar. Memory of the World
Digital Heritage through VR and Generative AI
ARCH 7036-04/ARCH5051-04.Elective Theory Seminar, SAID, DAAP, Spring 2025
Class time: Monday. 10 am -12:50 pm. Classroom: 4425-E, CGC Lab, DAAP building.
Faculty: Ming Tang, Professor in Architecture and Interior Design, DAAP. Director of Extended Reality Lab. XR-Lab
Seminar Description
This seminar invites students to explore the intersection of architecture, history, artificial intelligence, and immersive technologies by reimagining the past through digital heritage. Drawing on historic documents and archival materials from Cincinnati, students will work closely with historians to reconstruct a Cincinnati neighborhood as it existed in the 1950s, allowing users to step back in time and experience its spaces and stories through immersive visualization and virtual reality (VR).
Throughout the semester, students will investigate how generative AI, virtual reality (VR), and interactive visualization can preserve and reinterpret cultural memory. Using Unreal Engine as the primary platform, participants will design interactive VR environments for mobile headsets, creating spaces where history, atmosphere, and narrative merge into immersive experiences. The seminar aligns with UNESCO’s Memory of the World initiative, emphasizing the preservation of documentary and architectural heritage for future generations.
In addition to a large collaborative group project, each student will conduct a samll individual research-based design investigation focused on a “lost” historic artifact—such as a forgotten art work or street furniture. Through AI-assisted modeling, reality capture, and digital prototyping, students will gain hands-on experience reconstructing the intangible layers of history while developing advanced technical and conceptual skills in digital heritage creation.
Human Digital Twin
High-Fidelity Digital Human Modeling and Visualization
This research investigates advanced methods for constructing high-fidelity digital humans, with a focus on both technical innovation and applied use in immersive environments. The project integrates generative artificial intelligence, image-based modeling, and visualization pipelines to advance realism, interactivity, and usability in human–computer interaction.
Aim 1. Conversational Digital Agents Driven by Large Language Models (LLMs).
The first aim is to utilize large language models (LLMs) as the core engine for conversational digital humans. By embedding LLM-driven reasoning into virtual agents, the project seeks to create responsive, adaptive, and context-aware “talking agents.” These agents will simulate naturalistic dialogue, provide interactive guidance, and adapt to user needs across diverse scenarios such as education, healthcare training, and collaborative design.
Aim 2. Photorealistic Skin and Visual Fidelity Through Scanned Data.
The second aim focuses on the visual accuracy of digital humans. High-resolution image scans will be processed to reconstruct human skin with detailed fidelity, including surface textures, translucency, and micro-geometric variations. The resulting models are capable of 4K photorealistic rendering (click image to view sample output), significantly enhancing realism in simulation and visualization. This fidelity is crucial for applications where nuanced perception—such as empathy, trust, or attentiveness—depends on subtle visual cues.
Significance.
By combining intelligent conversational capabilities with photorealistic appearance, this research advances the next generation of digital humans. The outcomes will support applications in extended reality (XR), therapeutic and clinical training, collaborative design education, and digital twin environments, where authenticity of both interaction and appearance directly influences user engagement and effectiveness
High-fidelity digital human.
