ALIVE: An Avatar-Lecture Interactive Video Engine with Content-Aware Retrieval for Real-Time Interaction

TL;DR

ALIVE addresses the challenge of transforming passive lecture videos into interactive, context-aware learning by delivering avatar-based explanations grounded in the current instructional context. It achieves this with a fully local, multimodal pipeline that combines pause-triggered, content-aware retrieval with offline LLM reasoning and segmented avatar synthesis, all running on-device to preserve privacy. The system is evaluated on a medical imaging course, showing accurate grounding, low latency, and engaging avatar delivery, while ablations highlight the importance of timestamp-aware retrieval and segmented avatar generation. This framework demonstrates a practical path toward private, scalable, next-generation interactive learning environments that maintain instructor presence and instructional continuity.

Abstract

Traditional lecture videos offer flexibility but lack mechanisms for real-time clarification, forcing learners to search externally when confusion arises. Recent advances in large language models and neural avatars provide new opportunities for interactive learning, yet existing systems typically lack lecture awareness, rely on cloud-based services, or fail to integrate retrieval and avatar-delivered explanations in a unified, privacy-preserving pipeline. We present ALIVE, an Avatar-Lecture Interactive Video Engine that transforms passive lecture viewing into a dynamic, real-time learning experience. ALIVE operates fully on local hardware and integrates (1) Avatar-delivered lecture generated through ASR transcription, LLM refinement, and neural talking-head synthesis; (2) A content-aware retrieval mechanism that combines semantic similarity with timestamp alignment to surface contextually relevant lecture segments; and (3) Real-time multimodal interaction, enabling students to pause the lecture, ask questions through text or voice, and receive grounded explanations either as text or as avatar-delivered responses. To maintain responsiveness, ALIVE employs lightweight embedding models, FAISS-based retrieval, and segmented avatar synthesis with progressive preloading. We demonstrate the system on a complete medical imaging course, evaluate its retrieval accuracy, latency characteristics, and user experience, and show that ALIVE provides accurate, content-aware, and engaging real-time support. ALIVE illustrates how multimodal AI-when combined with content-aware retrieval and local deployment-can significantly enhance the pedagogical value of recorded lectures, offering an extensible pathway toward next-generation interactive learning environments.

Figures (10)

• Figure 1: System overview of ALIVE, illustrating the fully local, content-aware retrieval and segmented avatar synthesis pipeline that enables real-time interactive lecture engagement.
• Figure 2: Offline lecture preparation pipeline, showing how ASR transcription, transcript refinement, and time-aligned segmentation construct a retrieval-ready lecture index for content-aware interaction.
• Figure 3: Pause-triggered, content-aware question answering workflow, demonstrating how semantic similarity and timestamp alignment ground student queries in the relevant lecture context.
• Figure 4: Segmented avatar-delivered response generation pipeline, highlighting how text-to-speech, neural talking-head synthesis, and progressive preloading reduce perceived latency during explanation delivery.
• Figure 5: Baseline avatar-delivered lecture playback interface, providing a consistent instructor presence that anchors subsequent interactive and avatar-delivered explanations.