# PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation

arXiv: https://arxiv.org/abs/2605.07496 · HTML: https://mlsanity.com/paper/2605.07496/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2026.

## Built on by (most cited first, 1 of 1)

- [DiffWAM: A Fast and Efficient Navigation World Action Model](https://mlsanity.com/paper/2609.39763/) (2026)

## Abstract (arXiv)

Bird's-eye-view (BEV) images have been demonstrated to provide valuable prior information for navigation. Given the global information provided by such views, two key challenges remain: how to fully exploit this information and how to reliably use it during execution. In this paper, we propose a navigation system that uses BEV images as global priors and is designed for ground and near-ground robotic platforms. The system employs an image generation model to interpret human intent from natural language, identify the target destination, and generate traversability masks. During execution, we introduce cross-view localization to align the robot's odometry with the BEV map and mitigate long-term drift in conventional odometry. We conduct extensive benchmark experiments to evaluate the proposed method and further validate its effectiveness on both UAV and quadruped robot platforms. Using only a conventional local motion planner, the quadruped robot successfully completes a 285-meter outdoor long-range navigation task. This work demonstrates how the world-understanding capabilities of foundation models can be transferred to embodied navigation, enabling robots to benefit from the strong generalization ability of existing image generation models.