arXiv subjects

David Ledo

Publications and source records attributed to David Ledo.

3 recordsLinked to original sources

arXiv2026

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

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.

cs.HC↗

arXiv2026

Protosampling: Enabling Free-Form Convergence of Sampling and Prototyping through Canvas-Driven Visual AI Generation

As an emergent process, creativity relies on explorations via sampling and prototyping for problem construction. These activities compile knowledge, provide a context enveloping the solution, and answer questions. With Generative AI, practitioners can go beyond sampling existing media towards instantly generating and remixing new ones. We refer to this convergence as 'protosampling'. Using existing literature we ground a definition for protosampling and operationalize it through Atelier, a canvas-like system that leverages a variety of generative image and video models for visual creation. Atelier: (1) blends the spaces for thinking and creation, where both references and generated assets co-exist in one space, (2) provides various encapsulated technical workflows that focus on the activity at hand, and (3) enables navigating emergence through interactive visualizations, smart search, and collections. Protosampling as a lens reframes creative work to emphasize the process itself and how seemingly disjointed thoughts can tightly interweave into a final solution.

cs.HC↗

arXiv2024

StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning

Automated plot generation for games enhances the player's experience by providing rich and immersive narrative experience that adapts to the player's actions. Traditional approaches adopt a symbolic narrative planning method which limits the scale and complexity of the generated plot by requiring extensive knowledge engineering work. Recent advancements use Large Language Models (LLMs) to drive the behavior of virtual characters, allowing plots to emerge from interactions between characters and their environments. However, the emergent nature of such decentralized plot generation makes it difficult for authors to direct plot progression. We propose a novel plot creation workflow that mediates between a writer's authorial intent and the emergent behaviors from LLM-driven character simulation, through a novel authorial structure called "abstract acts". The writers define high-level plot outlines that are later transformed into concrete character action sequences via an LLM-based narrative planning process, based on the game world state. The process creates "living stories" that dynamically adapt to various game world states, resulting in narratives co-created by the author, character simulation, and player. We present StoryVerse as a proof-of-concept system to demonstrate this plot creation workflow. We showcase the versatility of our approach with examples in different stories and game environments.

cs.HC↗