mlsanityData to 7 Oct 2026

The GEST-Engine: From Event Graphs to Synthetic Video. A Full Technical Report

arXiv 2607.12231 ↗First posted 14 Jul 2026cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

We present the GEST-Engine, a complete system that goes from natural-language text to fully-annotated multi-actor video. At its core is an explicit world model: rather than encoding state as a learned latent, the engine maintains a complete, inspectable representation of the world (which actors exist, where they are, what they are doing, which objects they hold, and how events relate in time and space), expressed as a formal Graph of Events in Space and Time (GEST) and realized deterministically inside the open world of a commercial game engine driven through an open-source multiplayer scripting framework. GESTs are produced either procedurally or by an agentic text-to-GEST system in which an LLM Director plans a story through tool calls validated by a programmatic state backend, so every generated specification is executable by construction. A GEST then enters a four-stage execution pipeline: graph parsing and validation, entity and action grounding, temporal orchestration (Allen-style constraints resolved by Floyd-Warshall transitive closure), and execution and capture. In a single simulation pass the engine emits frame-aligned RGB video, dense per-pixel depth, instance segmentation, per-actor skeletal pose, per-frame pairwise spatial-relation graphs, 2D bounding boxes, event-to-frame temporal mappings, and natural-language descriptions, all at zero marginal annotation cost. We further describe an in-game world editor, runtime capability extraction, a text-generation pipeline, and a production system that renders corpora at scale across parallel virtual machines. Because every frame traces back to a semantic specification, the engine guarantees object permanence, multi-actor coordination, and temporal consistency by construction, making its output valuable as training data, evaluation benchmarks, and diagnostic tools for video understanding.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 14 Jul 2026.

Builds on

0 of 0 references.

No references here yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Authoring for Living Worlds: Tool-Constrained LLM Agents for Executable Multi-Actor Scenarios
  2. 2026From Pixels to States: Rethinking Interactive World Models as Game Engines
  3. 2026Oneira: From Open-Ended Generation to Open-World Interaction in Video World Models
  4. 2026From Zero to Hero: Training-Free Custom Concept Spawning in World Models
  5. 2025Simulating the Visual World with Artificial Intelligence: A Roadmap
  6. 2026MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines
  7. 2026ROWBench: Do Video Models Render What the Program Specifies?
  8. 2026Programmable World Model