PanoGen++: Domain-adapted text-guided panoramic environment generation for vision-and-language navigation
Open accessAbstract
Vision-and-language navigation (VLN) tasks require agents to navigate three-dimensional environments guided by natural language instructions, offering substantial potential for diverse applications. However, the scarcity of training data impedes progress in this field. This paper introduces PanoGen++, a novel framework that addresses this limitation by generating varied and pertinent panoramic environments for VLN tasks. PanoGen++ incorporates pre-trained diffusion models with domain-specific fine-tuning, employing parameter-efficient techniques such as low-rank adaptation to minimize computational costs. We investigate two settings for environment generation: masked image inpainting and recursive image outpainting. The former maximizes novel environment creation by inpainting masked regions based on textual descriptions, while the latter facilitates agents' learning of spatial relationships within panoramas. Empirical evaluations on room-to-room (R2R), room-for-room (R4R), and cooperative vision-and-dialog navigation (CVDN) datasets reveal significant performance enhancements: a 2.44% increase in success rate on the R2R test leaderboard, a 0.63% improvement on the R4R validation unseen set, and a 0.75-meter enhancement in goal progress on the CVDN validation unseen set. PanoGen++ augments the diversity and relevance of training environments, resulting in improved generalization and efficacy in VLN tasks.
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Low-quality data challenges include noise pollution, missing modalities, modal quality imbalance, and dynamic changes in modal quality, and are widely found in autonomous driving, medical image analysis, and speech recognition.
“The challenges of low-quality data are mainly reflected in various aspects such as noise pollution, missing modalities, modal quality imbalance, and dynamic changes in modal quality, etc., which are widely found in many application scenarios such as autonomous driving [81,88,257], medical image analysis, speech recognition [296], etc., and pose great challenges to the validity and reliability of”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [296]
The Limitation and Discussion section (p. 11) describes blurriness and geometric inconsistencies in generated panoramas, while the Comparisons section (p. 8) mentions Cityscapes only as a dataset not used for training. Searches for the listed low-quality-data challenges and their occurrence in autonomous driving, medical image analysis, or speech recognition found no passage addressing that claim. (LLM, conf=0.76)
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- Authors
- Wang, S., Zhou, D., Xie, L., Xu, C., Yan, Y., & Yin, E.
- Affiliations
- Tianjin University (CN)
- Harbin Institute of Technology (CN)
- PLA Academy of Military Science (CN)
- Corresponding author
- Zhou, Dongliang
- Yin, Erwei
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2025-07
- Volume
- 187
- Pages
- 107320
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W4408287837
- Cited by
- 11
- References
- 55
- Citations by year
- 2026: 6 · 2025: 5
- FWCI
- 3.89
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2023YFF1203900, 2023YFF1203903)
- Ministry of Science and Technology of the People's Republic of China
- National Natural Science Foundation of China (62332019, 62076250)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Keywords
- vision-and-language navigation, text-to-image generation, diffusion models, low-rank adaptation, domain adaptation, limited training data
- MeSH terms
- Humans, Language, Neural Networks, Computer, Spatial Navigation
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