# VISTA: Generative Visual Imagination for Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2505.07868 · HTML: https://mlsanity.com/paper/2505.07868/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2025.

## Built on by (most cited first, 9 of 9)

- [World Model for Robot Learning: A Comprehensive Survey](https://mlsanity.com/paper/2605.00080/) (2026)
- [VISTAv2: World Imagination for Indoor Vision-and-Language Navigation](https://mlsanity.com/paper/2512.00041/) (2025)
- [textscNaVIDA: Vision-Language Navigation with Inverse Dynamics Augmentation](https://mlsanity.com/paper/2601.18188/) (2026)
- [FantasyVLN: Unified Multimodal Chain-of-Thought Reasoning for Vision-Language Navigation](https://mlsanity.com/paper/2601.13976/) (2026)
- [Schr"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation](https://mlsanity.com/paper/2512.21201/) (2025)
- [Hypothesis Graph Refinement: Hypothesis-Driven Exploration with Cascade Error Correction for Embodied Navigation](https://mlsanity.com/paper/2604.04108/) (2026)
- [World Models for Embodied Intelligence: From Plausible to Controllable to Actionable](https://mlsanity.com/paper/2609.16697/) (2026)
- [LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning](https://mlsanity.com/paper/2603.29165/) (2026)
- [Beyond Thinking: Imagining in 360^circ for Humanoid Visual Search](https://mlsanity.com/paper/2605.09146/) (2026)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) tasks agents with locating specific objects in unseen environments using natural language instructions and visual cues. Many existing VLN approaches typically follow an 'observe-and-reason' schema, that is, agents observe the environment and decide on the next action to take based on the visual observations of their surroundings. They often face challenges in long-horizon scenarios due to limitations in immediate observation and vision-language modality gaps. To overcome this, we present VISTA, a novel framework that employs an 'imagine-and-align' navigation strategy. Specifically, we leverage the generative prior of pre-trained diffusion models for dynamic visual imagination conditioned on both local observations and high-level language instructions. A Perceptual Alignment Filter module then grounds these goal imaginations against current observations, guiding an interpretable and structured reasoning process for action selection. Experiments show that VISTA sets new state-of-the-art results on Room-to-Room (R2R) and RoboTHOR benchmarks, e.g.,+3.6% increase in Success Rate on R2R. Extensive ablation analysis underscores the value of integrating forward-looking imagination, perceptual alignment, and structured reasoning for robust navigation in long-horizon environments.