Dynamic Learning Solutions: A System for Personalized Educational Video Generation
- URL: http://arxiv.org/abs/2609.14408v1
- Date: Sun, 13 Sep 2026 10:13:10 GMT
- Title: Dynamic Learning Solutions: A System for Personalized Educational Video Generation
- Abstract summary: We present an automated pipeline that converts NCERT textbooks into interactive video explanations.<n>A user uploads a PDF and asks a question; the system then generates a video-based explanation as output.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present an automated pipeline that converts NCERT textbooks into interactive video explanations that respond directly to user queries. A user uploads a PDF and asks a question; the system then generates a video-based explanation as output, handling both text and visual elements from the PDF for multi-modal retrieval and response generation. The pipeline combines a Retrieval-Augmented Generation (RAG) model with generative multimedia components. The RAG stage is optimized for the structure of NCERT textbooks and performs best on content from those books. Given a user query, the RAG model retrieves relevant content from the PDF and generates a multi-scene script containing narrative explanations and structured visual prompts aligned with the textbook's explanatory style. These prompts are passed to a Stable Diffusion module, implemented layer by layer for interpretability and control, which generates contextually relevant images. The images are then processed by DynamiCrafter to produce animated sequences. Finally, a Google Text-to-Speech module generates synchronized narration, aligning speech with the visual scenes through time-based control. The result is a coherent video explanation integrating animation, narration, and textbook-aligned visuals, transforming static educational material into an engaging learning experience. By combining multi-modal document retrieval, generative visual models, animation frameworks, and speech synthesis, this pipeline demonstrates a scalable approach to delivering interactive, personalized digital education content.
Related papers
- Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment [20.811984469709508]
Aura is a unified framework for high-fidelity and identity-consistent video generation.<n>We introduce AI director-level captions that provide dense and structured descriptions of video content.<n>We build a high-quality video-subject image dataset through a dedicated data construction pipeline.
arXiv Detail & Related papers (2026-07-05T13:54:33Z) - EagleNet: Energy-Aware Fine-Grained Relationship Learning Network for Text-Video Retrieval [59.858268313337355]
We introduce Energy-Aware Fine-Grained Relationship Learning Network (EagleNet)<n>EagleNet generates accurate and context-aware enriched text embeddings.<n>Experiments have demonstrated the superiority of EagleNet across MSRVTT, DiDeMo, MSVD, and VATEX.
arXiv Detail & Related papers (2026-03-26T10:05:19Z) - Bridging Your Imagination with Audio-Video Generation via a Unified Director [54.45375287950375]
We argue that logical reasoning and imaginative thinking are both fundamental qualities of a film director.<n>We propose UniMAGE, a unified director model that bridges user prompts with well-structured scripts.
arXiv Detail & Related papers (2025-12-29T05:56:22Z) - DynaStride: Dynamic Stride Windowing with MMCoT for Instructional Multi-Scene Captioning [3.47287766500271]
Scene-level captioning in instructional videos can enhance learning by requiring an understanding of both visual cues and temporal structure.<n>We introduce DynaStride, a pipeline to generate coherent, scene-level captions without requiring manual scene segmentation.<n>We show that DynaStride produces captions that are more temporally coherent and informative, suggesting a promising direction for improving AI-powered instructional content generation.
arXiv Detail & Related papers (2025-10-27T22:29:08Z) - VTD-CLIP: Video-to-Text Discretization via Prompting CLIP [44.51452778561945]
Vision-language models bridge visual and linguistic understanding and have proven to be powerful for video recognition tasks.<n>Existing approaches rely primarily on parameter-efficient fine-tuning of image-text pre-trained models.<n>We propose a video-to-text discretization framework to address limited interpretability and poor generalization due to inadequate temporal modeling.
arXiv Detail & Related papers (2025-03-24T07:27:19Z) - Text-Conditioned Resampler For Long Form Video Understanding [94.81955667020867]
We present a text-conditioned video resampler (TCR) module that uses a pre-trained visual encoder and large language model (LLM)
TCR can process more than 100 frames at a time with plain attention and without optimised implementations.
arXiv Detail & Related papers (2023-12-19T06:42:47Z) - Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation [69.20173154096]
We develop a framework comprised of two functional modules, Motion Structure Retrieval and Structure-Guided Text-to-Video Synthesis.
For the first module, we leverage an off-the-shelf video retrieval system and extract video depths as motion structure.
For the second module, we propose a controllable video generation model that offers flexible controls over structure and characters.
arXiv Detail & Related papers (2023-07-13T17:57:13Z) - Emu: Generative Pretraining in Multimodality [43.759593451544546]
Transformer-based multimodal foundation model can seamlessly generate images and texts in multimodal context.
Emu can serve as a generalist multimodal interface for both image-to-text and text-to-image tasks.
Emu demonstrates superb performance compared to state-of-the-art large multimodal models.
arXiv Detail & Related papers (2023-07-11T12:45:39Z) - VX2TEXT: End-to-End Learning of Video-Based Text Generation From
Multimodal Inputs [103.99315770490163]
We present a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio.
Experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks.
arXiv Detail & Related papers (2021-01-28T15:22:36Z) - Dynamic Graph Representation Learning for Video Dialog via Multi-Modal
Shuffled Transformers [89.00926092864368]
We present a semantics-controlled multi-modal shuffled Transformer reasoning framework for the audio-visual scene aware dialog task.
We also present a novel dynamic scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing-semantic graph representations for every frame.
Our results demonstrate state-of-the-art performances on all evaluation metrics.
arXiv Detail & Related papers (2020-07-08T02:00:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.