Idea
Interactive video generation platform enabling real-time 1080P/60FPS simulation and text-driven editing for creators and developers
Research Paper
Core Innovation
This paper presents Yan, a unified framework combining real-time 3D simulation with multi-modal video diffusion and multi-granularity editing. It uniquely enables high-resolution, high-frame-rate interactive video generation with strong cross-domain generalization. The approach separates mechanics simulation from rendering, allowing flexible text-driven video editing unlike prior monolithic models.
Market Size (TAM)
$10–20B TAM, $2–5B SAM; assumption: growing demand for interactive video content across entertainment, marketing, and AR/VR sectors.
Potential Customers & Pain Points
- Video game developers needing real-time interactive content generation
- Film and animation studios seeking efficient video editing
- AR/VR content creators requiring high-fidelity simulations
- Marketing agencies wanting customizable video ads
- AI researchers exploring video synthesis
Business Model
Subscription-based SaaS platform offering tiered access to interactive video generation tools and API integrations for developers
Competitive Landscape
- Runway ML
- Synthesia
- DeepMotion
Implementation Challenges
- High computational resource requirements for real-time processing
- Complex integration of 3D simulation and diffusion models
- User adoption due to learning curve for multi-granularity editing
Validation Strategy
- Develop prototype demonstrating real-time 1080P/60FPS interactive video generation
- Pilot with select game studios and content creators for feedback
- Measure user engagement and editing efficiency improvements
Research Paper Overview
Yan: Foundational Interactive Video Generation
Summary
Yan is a comprehensive framework for interactive video generation that integrates real-time 3D simulation, multi-modal video diffusion models, and multi-granularity video editing. It enables real-time 1080P/60FPS interactive simulation using a compressed 3D-VAE and KV-cache denoising, supports frame-wise, action-controllable infinite video generation with strong cross-domain generalization, and allows text-driven video content editing by disentangling mechanics simulation from rendering.