PrevizWhiz: Combining Rough 3D Scenes and 2D Video to Guide Generative Video Previsualization

2026-02-03

AI Summary

This work proposes a novel approach to cinematic previsualization that bridges the gap between efficiency and expressiveness by integrating coarse 3D layouts with generative image and video models. Traditional methods—such as hand-drawn storyboards lacking spatial accuracy or high-fidelity 3D previs requiring costly assets and specialized expertise—are circumvented through frame-level style transfer, motion-path-driven temporal editing, and external video-guided synthesis. The resulting system enables high-fidelity, stylized previsualization with significantly reduced production barriers, accelerating creative iteration and improving communication between directors and technical teams. User studies demonstrate its effectiveness in enhancing previs efficiency and supporting rapid exploration, while also highlighting critical challenges in AI-assisted creation concerning visual continuity, authorship attribution, and ethical considerations.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

In pre-production, filmmakers and 3D animation experts must rapidly prototype ideas to explore a film's possibilities before fullscale production, yet conventional approaches involve trade-offs in efficiency and expressiveness. Hand-drawn storyboards often lack spatial precision needed for complex cinematography, while 3D previsualization demands expertise and high-quality rigged assets. To address this gap, we present PrevizWhiz, a system that leverages rough 3D scenes in combination with generative image and video models to create stylized video previews. The workflow integrates frame-level image restyling with adjustable resemblance, time-based editing through motion paths or external video inputs, and refinement into high-fidelity video clips. A study with filmmakers demonstrates that our system lowers technical barriers for film-makers, accelerates creative iteration, and effectively bridges the communication gap, while also surfacing challenges of continuity, authorship, and ethical consideration in AI-assisted filmmaking.

Problem

• previsualization
• filmmaking
• 3D animation
• storyboarding
• generative video

Innovation

• generative video
• previsualization
• 3D scene
• image restyling
• AI-assisted filmmaking