BlendScape | Bala Kumaravel

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.

Honorable Mention Award at UIST 2024

ACM-DL: https://dl.acm.org/doi/10.1145/3654777.3676326 ArXiv: https://arxiv.org/abs/2403.13947

Overview of BlendScape, a rendering and composition system for end-users to customize video-conference environments by leveraging AI image generation techniques.

Project video

References

2024

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

Shwetha Rajaram, Nels Numan, Balasaravanan Thoravi Kumaravel, Nicolai Marquardt, and Andrew D Wilson

In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology, 2024

Abs

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.