# SPICA: Interactive Video Content Exploration through Augmented Audio Descriptions for Blind or Low-Vision Viewers

arXiv: https://arxiv.org/abs/2402.07300 · HTML: https://mlsanity.com/paper/2402.07300/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2024.

## Built on by (most cited first, 5 of 5)

- [Vid2Coach: Transforming How-To Videos into Task Assistants](https://mlsanity.com/paper/2506.00717/) (2025)
- [VideoA11y: Method and Dataset for Accessible Video Description](https://mlsanity.com/paper/2502.20480/) (2025)
- [Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions](https://mlsanity.com/paper/2507.15692/) (2025)
- [Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies](https://mlsanity.com/paper/2410.08860/) (2024)
- [Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Player](https://mlsanity.com/paper/2602.04104/) (2026)

## Abstract (arXiv)

Blind or Low-Vision (BLV) users often rely on audio descriptions (AD) to access video content. However, conventional static ADs can leave out detailed information in videos, impose a high mental load, neglect the diverse needs and preferences of BLV users, and lack immersion. To tackle these challenges, we introduce SPICA, an AI-powered system that enables BLV users to interactively explore video content. Informed by prior empirical studies on BLV video consumption, SPICA offers novel interactive mechanisms for supporting temporal navigation of frame captions and spatial exploration of objects within key frames. Leveraging an audio-visual machine learning pipeline, SPICA augments existing ADs by adding interactivity, spatial sound effects, and individual object descriptions without requiring additional human annotation. Through a user study with 14 BLV participants, we evaluated the usability and usefulness of SPICA and explored user behaviors, preferences, and mental models when interacting with augmented ADs.