AI Powered Commentary and Camera Direction in E-Sports - Narayanan, Swathi Jamjal; Joseph, Kevin Winston; Sirohi, Devansh; Chaudhary, Harsh; Shivkumar, Hitesh (UNIR, 2026)
Real-time, AI-driven commentary and camera direction provide revolutionary possibilities to improve spectator engagement and comprehension of live events in the rapidly advancing world of e-sports. This paper proposes an autonomous system designed to both generate dynamic commentary as well as control the spectator camera for live-streamed e-sports matches, specifically focusing on League of Legends (LoL), a popular Multiplayer Online Battle Arena (MOBA) game. It incorporates the use of GPT-4o with Vision and OpenAI’s TTS API. Synchronization of commentary with real-time camera movements is one of the major challenges tackled. This is done using a camera tracking and scene change detection algorithm that effectively adjusts the commentary to changing scenes in real-time by utilizing computer vision techniques. Further, two neural architectures for AI-driven camera control: a 2D Convolutional-LSTM (Conv-LSTM) model that concentrates on independent spatial and temporal analysis, and a 3D CNN model that combines these features to forecast camera movements in a more comprehensive way are presented. Evaluations on fluency, relevance, and strategic depth metrics, show that our integrated system improves viewer experience by providing deep and coherent narratives that are contextually aligned with the game dynamics. The proposed models are evaluated quantitatively in capturing spectator camera movement patterns.
Keywords: AI-driven Commentary; Camera Control; Computer Vision; E- sports Analytics; Neural Architectures