Chat Modeling: Interaction-Enhanced Agent Framework for Visualizing Literature-Grounded Biological Structures

Fuente: arXiv
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Main Authors: Jia, Donggang, Wang, Yunhai, Viola, Ivan
Format: Preprint
Published: 2024
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author Jia, Donggang
Wang, Yunhai
Viola, Ivan
author_facet Jia, Donggang
Wang, Yunhai
Viola, Ivan
contents Bioscientists frequently seek to visualize the biological systems they have empirically characterized and reported in the literature. Realizing such visualizations requires biological structure modeling, an inherently complex process that demands both biological and geometric understanding. This paper addresses the problem of constructing such 3D models for visualization. In this paper, we introduce a novel agent framework that mitigates the challenges of operating 3D modeling software by transforming user inputs, including natural language descriptions, research publication content, and textual descriptions of the existing objects and structures in the current scene, into modeling operations in a structured JSON format and final 3D results. The major technical contribution lies in the collaborative agent design that simultaneously supports model planning, execution, and novel user interaction design, such as interactive modeling execution and dynamic widget generation that fuse text and mouse interaction within the chat window. The framework further incorporates a customized modeling memory to enhance user interaction, featuring components such as personalized memory management, feedback collection, and skill library design. This modeling memory is leveraged to enable improved 3D modeling performance over time. The quantitative evaluation on our collected dataset showcases the effectiveness of our framework. We also develop a prototype tool, Chat Modeling, and demonstrate its usage through two modeling case studies. Our user study and expert interviews highlight the potential of our approach for use in scientific workflows.
format Preprint
id arxiv_https___arxiv_org_abs_2404_01063
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Chat Modeling: Interaction-Enhanced Agent Framework for Visualizing Literature-Grounded Biological Structures
Jia, Donggang
Wang, Yunhai
Viola, Ivan
Human-Computer Interaction
Graphics
Bioscientists frequently seek to visualize the biological systems they have empirically characterized and reported in the literature. Realizing such visualizations requires biological structure modeling, an inherently complex process that demands both biological and geometric understanding. This paper addresses the problem of constructing such 3D models for visualization. In this paper, we introduce a novel agent framework that mitigates the challenges of operating 3D modeling software by transforming user inputs, including natural language descriptions, research publication content, and textual descriptions of the existing objects and structures in the current scene, into modeling operations in a structured JSON format and final 3D results. The major technical contribution lies in the collaborative agent design that simultaneously supports model planning, execution, and novel user interaction design, such as interactive modeling execution and dynamic widget generation that fuse text and mouse interaction within the chat window. The framework further incorporates a customized modeling memory to enhance user interaction, featuring components such as personalized memory management, feedback collection, and skill library design. This modeling memory is leveraged to enable improved 3D modeling performance over time. The quantitative evaluation on our collected dataset showcases the effectiveness of our framework. We also develop a prototype tool, Chat Modeling, and demonstrate its usage through two modeling case studies. Our user study and expert interviews highlight the potential of our approach for use in scientific workflows.
title Chat Modeling: Interaction-Enhanced Agent Framework for Visualizing Literature-Grounded Biological Structures
topic Human-Computer Interaction
Graphics
url https://arxiv.org/abs/2404.01063