AI Creates How-To Videos from Text and Existing Footage
VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting
By
Muhammet Furkan Ilaslan|Ali Koksal|Kevin Qinhong Lin|Burak Satar|Mike Zheng Shou|Qianli Xu

https://arxiv.org/abs/2412.11621v1
Summary
Imagine typing in "How to make a perfect omelet" and getting a concise, instructional video, complete with helpful visuals, generated on the fly. That's the promise of a new AI framework called VG-TVP (Visually Grounded Text-Video Prompting). Researchers have developed this innovative system to create multimodal procedural plans, essentially "how-to" guides that combine text and video instructions. It works by taking a text prompt, like "How to change a tire," and leveraging existing instructional videos on the topic. The system cleverly analyzes these videos, extracts key steps, and then uses AI to generate new video segments that align with the user’s specific request. These generated videos are then seamlessly integrated with text instructions, creating a coherent and easy-to-follow guide. What sets VG-TVP apart is its ability to create dynamic, action-based video instructions, unlike previous methods that focused on static images. By incorporating human-centric actions, like showing someone actually changing the tire, the system closes the cognitive gap between instruction and execution, making learning a new procedure more intuitive. To evaluate VG-TVP, the team created a new dataset called Daily-PP, containing a diverse range of everyday tasks like cooking, crafting, and home repairs. Tests showed VG-TVP significantly outperforms traditional methods, delivering more informative and accurate procedural plans, especially for tasks with subtle nuances. VG-TVP isn't just for everyday tasks. It has the potential to revolutionize fields like education, training, and even healthcare, offering a dynamic and personalized way to learn complex procedures. While the technology is still in its early stages, it highlights the exciting possibilities of using AI to make learning more accessible and engaging. Future research is focusing on how these generated instructions improve human learning outcomes and on expanding the range of tasks VG-TVP can handle. Imagine a future where you can master any skill, from cooking a complex dish to assembling furniture, simply by typing a request. VG-TVP brings us one step closer to that reality.
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How does VG-TVP's video generation system technically work to create instructional content?
VG-TVP processes instructional content through a multi-stage pipeline. First, it analyzes the text prompt and searches through existing instructional videos to identify relevant segments. The system then employs AI algorithms to extract key procedural steps, analyzing both visual and temporal elements of the source videos. These components are processed through a multimodal framework that aligns text instructions with corresponding video segments. Finally, it generates new video content by seamlessly integrating these elements into a coherent instructional sequence. For example, when creating a 'how to make coffee' video, it might combine footage showing proper bean grinding techniques, optimal water temperature demonstration, and precise pouring methods from various sources into one cohesive tutorial.
What are the main benefits of AI-powered instructional videos compared to traditional learning methods?
AI-powered instructional videos offer several key advantages over traditional learning methods. They provide personalized, on-demand learning experiences that can be tailored to individual needs and pace. The dynamic combination of visual demonstrations and text instructions helps bridge the gap between theory and practice, making complex procedures easier to understand. These videos can be particularly beneficial in professional training, educational settings, and self-learning scenarios. For instance, students can learn at their own pace, professionals can quickly master new skills, and hobbyists can access expert-level instruction for various crafts or activities anytime, anywhere.
How might AI-generated how-to videos transform the future of online learning and skill development?
AI-generated how-to videos are poised to revolutionize online learning by making skill acquisition more accessible and efficient. This technology could enable instant creation of customized tutorials for virtually any task, from simple home repairs to complex professional procedures. The ability to generate clear, step-by-step visual instructions on demand could democratize knowledge sharing and accelerate skill development across various fields. Industries like employee training, education, and professional development could benefit from more engaging, consistent, and scalable learning solutions. This could lead to a future where anyone can access expert-level instruction for any skill simply by requesting it.
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PromptLayer Features
- Testing & Evaluation
- VG-TVP's evaluation against the Daily-PP dataset aligns with PromptLayer's testing capabilities for measuring output quality and consistency
Implementation Details
Create test suites comparing generated video instructions against known good examples, using metrics for visual coherence and step accuracy
Key Benefits
• Systematic evaluation of video instruction quality
• Reproducible testing across different task types
• Quantifiable performance metrics for comparison
Potential Improvements
• Add specialized metrics for visual content evaluation
• Implement user feedback integration
• Develop automated regression testing for visual outputs
Business Value
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Efficiency Gains
50% faster validation of generated instructional content
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Cost Savings
Reduced need for manual quality assurance review
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Quality Improvement
More consistent and reliable instructional outputs
- Analytics
- Workflow Management
- The multi-step process of analyzing videos and generating new content maps to PromptLayer's workflow orchestration capabilities
Implementation Details
Create workflow templates for video analysis, content extraction, and instruction generation steps
Key Benefits
• Streamlined content generation pipeline
• Versioned workflow templates
• Reusable components across different instruction types
Potential Improvements
• Add specialized video processing steps
• Implement parallel processing capabilities
• Create domain-specific workflow templates
Business Value
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Efficiency Gains
40% faster instruction generation process
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Cost Savings
Reduced operational overhead through automation
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Quality Improvement
More consistent instruction generation across different tasks