HCII at IUI 2026

July 13, 2026

Researchers share advancements in AI-driven interfaces 

The annual Association of Computing Machinery (ACM) Conference on Intelligent User Interfaces (IUI) will take place July 13-16, 2026 in Limassol, Cyprus. 

Research at IUI focuses on the intersection of Artificial Intelligence (AI) and Human-Computer Interaction (HCI) and covers both the computational and human-centered aspects of modern interface design and development. 

A search for Carnegie Mellon University in the IUI program returns a full list of accepted papers and workshops at IUI 2026 from CMU researchers. Authors from CMU's Human-Computer Interaction Institute contributed to two accepted papers, both listed below.  
 

Visual Lyrics: Generating Animated Text for Music Lyric Videos with an Augmented Text Editor

David Chuan-En Lin, Cuong Nguyen (Adobe Research), Hijung Valentina Shin (Adobe Research), Nikolas Martelaro

🔗 Paper   

🔗 Project page  

Abstract: Animated lyric videos transform song lyrics into dynamic visual experiences, offering a powerful medium for artistic expression and audience engagement. However, creating these videos is challenging, requiring expertise in audio, typography, graphic design, and animation, making it inaccessible to novices. To address this challenge, we introduce Visual Lyrics, a proof-of-concept system for generating animated lyric videos controlled with an augmented text editor interface. We examined existing lyric videos to distill a taxonomy and design guidelines, informing the design of Visual Lyrics. Our key insight is a multimodal music analysis pipeline based on the taxonomy and leveraging LLM’s strong natural language understanding and code generation capabilities to synthesize creative and semantically meaningful animations. We collected a dataset of over 300 code-driven creative text animations to serve as inspiration for our LLM-driven pipeline, which we open source. In a user study, Visual Lyrics enabled novices to easily create high-quality animated lyric videos with high ratings of enjoyment, inspiration, and exploration.

ConverSearch: Supporting Experts in Human Behavior Analysis of Conversational Videos with a Multimodal Scene Search Tool (TIIS)

Riku Arakawa, Kiyosu Maeda, Hiromu Yakura (Max-Planck Institute for Human Development) 

🔗 Paper

Abstract: Multimodal scene search of conversations is essential for unlocking valuable insights into social dynamics and enhancing our communication. While experts in conversational analysis have their own knowledge and skills to find key scenes, a lack of comprehensive, user-friendly tools that streamline the processing of diverse multimodal queries impedes efficiency and objectivity. To address this gap, we developed ConverSearch, a visual-programming-based tool based on insights for effective interface and implementation design derived from a formative study with experts. The tool allows experts to integrate various machine learning algorithms to capture human behavioral cues without the need for coding. Our user study, employing the System Usability Scale (SUS) and satisfaction metrics, demonstrated high user preference, reflecting the tool’s ease of use and effectiveness in supporting scene search tasks. Additionally, through a deployment trial within industrial organizations, we confirmed the tool’s objectivity, reusability, and potential to enhance expert workflows. This suggests the advantages of expert-AI collaboration in domains requiring human contextual understanding and demonstrates how customizable, transparent tools yielding reusable artifacts can support expert-driven tasks in complex, multimodal environments.