---
license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
tags:
- spatial-reasoning
- cross-viewpoint localization
pretty_name: ViewSpatial-Bench
size_categories:
- 1K<n<10K
configs:
- config_name: ViewSpatial-Bench
  data_files:
  - split: test
    path: ViewSpatial-Bench.json
---
# Dataset Card for Dataset Name

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ViewSpatial-Bench is the first comprehensive benchmark designed specifically for evaluating multi-viewpoint spatial orientation recognition capabilities of vision-language models (VLMs) across five distinct task types. The benchmark assesses how well VLMs can perform spatial reasoning from different perspectives, focusing on both egocentric (camera) and allocentric (human subject) viewpoints.
The benchmark addresses a critical limitation in current VLMs: while they excel at egocentric spatial reasoning (from the camera's perspective), they struggle to generalize to allocentric viewpoints when required to adopt another entity's spatial frame of reference. This capability, known as "perspective-taking," is crucial for embodied interaction, spatial navigation, and multi-agent collaboration.
- **Language(s) (NLP):** en
- **License:** apache-2.0

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