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ACM CHI 2024

Experiential Views

Towards Human Experience Evaluation of Designed Spaces using Vision-Language Models

A screen capture of Experiential View’s floor plan visualization and WebGL-based 3D viewer integration user interface.

Abstract

Experiential Views is a proof-of-concept in which we explore a method of helping architects and designers predict how building occupants might experience their designed spaces using AI technology based on Vision-Language Models. Our prototype evaluates a space using a pre-trained model that we fine-tuned with photos and renders of a building. These images were evaluated and labeled based on a preliminary set of three human-centric dimensions that characterize the Social, Tranquil, and Inspirational qualities of a scene. We developed a floor plan visualization and a WebGL-based 3D-viewer that demonstrate how architectural design software could be enhanced to evaluate areas of a built environment based on psychological or emotional criteria. We see this as an early step towards helping designers anticipate emotional responses to their designs to create better experiences for occupants.

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Associated Researchers

Bon Adriel Aseniero

Senior Research Scientist

Michael Lee

Principal Research Engineer

Yi Wang

Principal Research Scientist

Qian Zhou

Principal Research Scientist

Nastaran Shahmansouri

Former Autodesk

Rhys Goldstein

Senior Principal Research Scientist
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