AI-based System for Transforming text and sound to Educational Videos
This addresses the challenge of creating interactive educational content for learners, though it appears incremental as it builds on existing deep learning techniques for video generation.
The paper tackles the problem of generating educational videos from text or sound inputs by proposing a novel GAN-based method that achieves a Fréchet Inception Distance (FID) score of 28.75%, indicating improved visual quality over existing systems like TGAN, MoCoGAN, and TGANS-C.
Technological developments have produced methods that can generate educational videos from input text or sound. Recently, the use of deep learning techniques for image and video generation has been widely explored, particularly in education. However, generating video content from conditional inputs such as text or speech remains a challenging area. In this paper, we introduce a novel method to the educational structure, Generative Adversarial Network (GAN), which develop frame-for-frame frameworks and are able to create full educational videos. The proposed system is structured into three main phases In the first phase, the input (either text or speech) is transcribed using speech recognition. In the second phase, key terms are extracted and relevant images are generated using advanced models such as CLIP and diffusion models to enhance visual quality and semantic alignment. In the final phase, the generated images are synthesized into a video format, integrated with either pre-recorded or synthesized sound, resulting in a fully interactive educational video. The proposed system is compared with other systems such as TGAN, MoCoGAN, and TGANS-C, achieving a Fréchet Inception Distance (FID) score of 28.75%, which indicates improved visual quality and better over existing methods.