Valerant: An Automatic Navigable Game Map Generator via Action-Conditioned World Model Exploration
Classification
View four quadrants- Major category
- Related resources
- Quadrant
- Not applicable
- Architecture
- Not applicable
- Prediction paradigm
- Not applicable
- Subcategories
- Generative-world applications
- Source review status
- Not assigned
Category review. An adjacent generative-world application: candidate-action video prediction, geometric validation and map reconstruction support exploration inside a generated game world. It provides ideas for WAM planning and memory, but no demonstrated robot control or policy transfer. The paper is retained for its adjacent resource/method role; WAM joint-prediction/IDM architecture quadrants do not apply to the cataloged contribution. Reading evidence
Contribution
Valerant expands one game screenshot into a persistent 3D point-cloud map by generating alternative action-conditioned videos, reconstructing each with SLAM, and committing the best admissible branch. A frozen video model supplies futures; an external exploration policy selects actions. Perceptual comparison and qualitative ablations support this prototype, while geometric reliability and reproducibility remain incompletely measured.
Abstract
An abstract has not been added yet.
Affiliations
Case Western Reserve University; Johns Hopkins University