BlendScape: Enabling End-User Customization of Video-Conferencing Environments through Generative AI

Today’s video-conferencing tools support a rich range of professional and social activities, but their generic meeting environments cannot be dynamically adapted to align with distributed collaborators’ needs. To enable end-user customization, we developed BlendScape, a rendering and composition system for video-conferencing participants to tailor environments to their meeting context by leveraging AI image generation techniques. BlendScape supports flexible representations of task spaces by blending users’ physical or digital backgrounds into unified environments and implements multimodal interaction techniques to steer the generation. Through an exploratory study with 15 end-users, we investigated whether and how they would find value in using generative AI to customize video-conferencing environments. Participants envisioned using a system like BlendScape to facilitate collaborative activities in the future, but required further controls to mitigate distracting or unrealistic visual elements. We implemented scenarios to demonstrate BlendScape’s expressiveness for supporting environment design strategies from prior work and propose composition techniques to improve the quality of environments.

Authors

Institutions

Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1145/3654777.3676326
Citations
10
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
2.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

BlendScape: Enabling End-User Customization of Video-Conferencing Environments through Generative AI

Nicolai Marquardt, Nels Numan, Shwetha Rajaram, Andrew D. Wilson et al.
10 citations
Data Visualization and Analytics
2.49
article

BlendScape: Enabling End-User Customization of Video-Conferencing Environments through Generative AI

Nicolai Marquardt, Nels Numan, Shwetha Rajaram, Andrew D. Wilson, Balasaravanan Thoravi Kumaravel
article en
10 citations

Abstract

Today’s video-conferencing tools support a rich range of professional and social activities, but their generic meeting environments cannot be dynamically adapted to align with distributed collaborators’ needs. To enable end-user customization, we developed BlendScape, a rendering and composition system for video-conferencing participants to tailor environments to their meeting context by leveraging AI image generation techniques. BlendScape supports flexible representations of task spaces by blending users’ physical or digital backgrounds into unified environments and implements multimodal interaction techniques to steer the generation. Through an exploratory study with 15 end-users, we investigated whether and how they would find value in using generative AI to customize video-conferencing environments. Participants envisioned using a system like BlendScape to facilitate collaborative activities in the future, but required further controls to mitigate distracting or unrealistic visual elements. We implemented scenarios to demonstrate BlendScape’s expressiveness for supporting environment design strategies from prior work and propose composition techniques to improve the quality of environments.

Microsoft (United States) (US), University of Michigan (US), Michigan United (US), Microsoft Research (United Kingdom) (GB), University College London (GB)
Openalex Percentile: Top 9%
Data Visualization and Analytics
2.49
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.