PrevizWhiz: Combining Rough 3D Scenes and 2D Video to Guide Generative Video Previsualization
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910010602160128 |
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| author | Hu, Erzhen Brudy, Frederik Ledo, David Fitzmaurice, George Anderson, Fraser |
| author_facet | Hu, Erzhen Brudy, Frederik Ledo, David Fitzmaurice, George Anderson, Fraser |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03838 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PrevizWhiz: Combining Rough 3D Scenes and 2D Video to Guide Generative Video Previsualization Hu, Erzhen Brudy, Frederik Ledo, David Fitzmaurice, George Anderson, Fraser Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition H.5.2; I.3.7; H.5.1 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. |
| title | PrevizWhiz: Combining Rough 3D Scenes and 2D Video to Guide Generative Video Previsualization |
| topic | Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition H.5.2; I.3.7; H.5.1 |
| url | https://arxiv.org/abs/2602.03838 |