| --- |
| 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} |
| } |
| ``` |
| |
