Visual Generative Artificial Intelligence (GenAI) in Mixed Reality (MR): A Survey of Techniques, Interaction Designs, and Evaluations

GenAI is increasingly reshaping spatial interactions in immersive environments, bridging diverse input and output modalities to create intelligent user experiences. This survey provides a structured examination of visual GenAI techniques adapted and applied to MR contexts, which include Virtual Reality (VR), and Augmented Reality (AR), emphasizing their roles in enabling rich, adaptive, and naturalistic interactions. We screened publications from international venues between 2018 and 2025, followed by an in-depth review of 102 papers. We focus on the applications of visual content creation in 2D&3D and vision-language models (VLMs), highlighting their integration practices within MR. Furthermore, we investigate the co-evolution of generative models and MR interaction design, including immersive visualization, controllable generation, and synthetic data creation. The survey concludes by outlining recent challenges and potential research directions to advance the synergy of visual GenAI and MR techniques.

Authors

Institutions

Publication Details

Journal
International Journal of Human-Computer Interaction
Published
2026-08-18
DOI
https://doi.org/10.1080/10447318.2026.2713822
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Visual Generative Artificial Intelligence (GenAI) in Mixed Reality (MR): A Survey of Techniques, Interaction Designs, and Evaluations

Linjie Qiu, Mingming Fan, Yue Jiang, Duotun Wang et al.
International Journal of Human-Computer Interaction
Multimodal Machine Learning Applications
article

Visual Generative Artificial Intelligence (GenAI) in Mixed Reality (MR): A Survey of Techniques, Interaction Designs, and Evaluations

Linjie Qiu, Mingming Fan, Yue Jiang, Duotun Wang, Zeyu Wang, Linlin Cai
article en

Abstract

GenAI is increasingly reshaping spatial interactions in immersive environments, bridging diverse input and output modalities to create intelligent user experiences. This survey provides a structured examination of visual GenAI techniques adapted and applied to MR contexts, which include Virtual Reality (VR), and Augmented Reality (AR), emphasizing their roles in enabling rich, adaptive, and naturalistic interactions. We screened publications from international venues between 2018 and 2025, followed by an in-depth review of 102 papers. We focus on the applications of visual content creation in 2D&3D and vision-language models (VLMs), highlighting their integration practices within MR. Furthermore, we investigate the co-evolution of generative models and MR interaction design, including immersive visualization, controllable generation, and synthetic data creation. The survey concludes by outlining recent challenges and potential research directions to advance the synergy of visual GenAI and MR techniques.

International Journal of Human-Computer Interaction
Hong Kong University of Science and Technology (HK), University of Utah (US), Machine Science (US), University of Hong Kong (HK)
Openalex Percentile: Top 11%
Multimodal Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.