---
license: cc-by-nc-4.0
pretty_name: SAW-Bench
language:
- en
task_categories:
- visual-question-answering
- video-text-to-text
tags:
- egocentric
- video
- spatial-reasoning
- situated-awareness
- embodied-ai
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: localization
    path: data/localization.parquet
  - split: direction
    path: data/direction.parquet
  - split: revplan
    path: data/revplan.parquet
  - split: shape
    path: data/shape.parquet
  - split: affordance
    path: data/affordance.parquet
  - split: memory
    path: data/memory.parquet
- config_name: all
  data_files:
  - split: test
    path: data/all.parquet
---

# SAW-Bench: Learning Situated Awareness in the Real World

**SAW-Bench** (**S**ituated **A**wareness in the Real **W**orld) is a benchmark for
evaluating *observer-centric* situated awareness in multimodal foundation models —
the ability to reason about space, motion, and possible actions relative to one's own
egocentric viewpoint as it evolves over time.

Unlike prior benchmarks that emphasize *environment-centric* relations (how objects
relate to each other in a scene), SAW-Bench probes whether a model can maintain a
coherent observer-centric spatial state from egocentric video. It comprises
**786 real-world videos** captured with Ray-Ban Meta (Gen 2) smart glasses and
**2,071 human-annotated question–answer pairs** across **six tasks**. Even the best
model trails humans by **37.66%**.

- 📄 Paper: [arXiv:2602.16682](http://arxiv.org/abs/2602.16682)
- 🌐 Project page: https://sawbench.github.io/
- 💻 Evaluation code: https://github.com/UCSB-AI/SAW-Bench

## Tasks

| Task (`task`)   | What it probes                                        | # QA |
| --------------- | ----------------------------------------------------- | ---: |
| `localization`  | Where am I within the space (corner / side / center)? |  200 |
| `direction`     | Where is a target relative to my current heading?     |  834 |
| `shape`         | What is the shape of the path I traveled?             |  546 |
| `revplan`       | How do I get back to where I started?                 |  229 |
| `memory`        | What changed in the scene between two visits?         |  100 |
| `affordance`    | What action is feasible from my current pose?         |  162 |
| **Total**       |                                                       | **2,071** |

## Dataset structure

```
ucsbai/SAW-Bench/
├── data/                       # one Parquet shard per task + all.parquet
│   ├── localization.parquet · direction.parquet · revplan.parquet
│   ├── shape.parquet · affordance.parquet · memory.parquet
│   └── all.parquet             # all tasks combined
└── videos_compressed/          # egocentric clips
    └── Scene_<id>/<key>.mp4
```

### Fields

| field            | type        | description                                               |
| ---------------- | ----------- | --------------------------------------------------------- |
| `task`           | string      | one of the six task names                                 |
| `id`             | int         | id within the task                                        |
| `question`       | string      | the multiple-choice question                              |
| `options`        | list[str]   | answer choices                                            |
| `ground_truth`   | string      | the correct option text                                   |
| `answer`         | int         | index of `ground_truth` within `options`                  |
| `key`            | string      | `"<scene>_<video>"`, maps to `videos_compressed/Scene_<scene>/<key>.mp4` |
| `scene_category` | string      | `indoor` or `outdoor`                                     |

## Usage

Load the QA pairs:

```python
from datasets import load_dataset

# one split per task
ds = load_dataset("ucsbai/SAW-Bench", split="direction")
print(ds[0])

# or all tasks combined
ds_all = load_dataset("ucsbai/SAW-Bench", "all", split="test")
```

Download the videos:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="ucsbai/SAW-Bench",
    repo_type="dataset",
    allow_patterns=["videos_compressed/**"],
    local_dir="SAW-Bench",
)
```

For the full evaluation pipeline (download → generate → parse → score), see the
[code repository](https://github.com/UCSB-AI/SAW-Bench).

## Ethics, privacy & responsible use

SAW-Bench consists of real-world egocentric videos. Please read this statement
before using the data.

**Collection & consent.** Videos were self-recorded by participants who consented
to wearing the camera (Ray-Ban Meta Gen 2 smart glasses) in everyday indoor and
outdoor environments. **Incidental third parties (e.g., passers-by) and
identifiable locations may appear** in the background; no individuals were
deliberately targeted, tracked, or directed.

**Privacy minimization.** Audio is removed from all clips. A face/identity-blurred
variant of the videos was produced during the study. Even so, faces, license
plates, or other identifying details may remain partially visible in some frames.

**Permitted use.** Non-commercial academic research only, under CC BY-NC 4.0.

**Prohibited use.** Do not attempt to identify, re-identify, locate, or contact any
individual in the videos; do not use the data to train or evaluate face-recognition,
biometric, surveillance, or person-tracking systems; do not use it commercially.

**Removal requests.** If you appear in a video, or are a rights holder, and want a
clip removed, contact **chuhan_li@ucsb.edu** and we will promptly remove it.

By downloading the data you agree to these terms.

## Citation

```bibtex
@inproceedings{li2026sawbench,
  title     = {{SAW}-Bench: Learning Situated Awareness in the Real World},
  author    = {Chuhan Li and Rilyn R. Han and Joy Hsu and Yongyuan Liang and
               Rajiv Dhawan and Jiajun Wu and Ming-Hsuan Yang and Xin Eric Wang},
  booktitle = {Forty-third International Conference on Machine Learning},
  year      = {2026},
  url       = {https://openreview.net/forum?id=8lwrYjv6r7}
}
```