WitneyWW/AEA-annotations

Aria Everyday Activities (AEA) annotations + SAVVY-Bench annotations

Three parts:

• subset_solo20/ — 20 curated single-person examples with videos, raw VRS, 7-channel mic-array audio and full spatial annotations (see below).
• aea_official/ — official per-recording annotations (no VRS) for 50 recordings.
• savvy/ — SAVVY-Bench ground-truth annotations.

subset_solo20/

20 task clips (from 18 recordings) in which one person carries out a whole task alone, step by step, with complete spatial annotations and where spatial audio matters (a large share of the sound reaches the wearer from the side or from behind).

Selection

1. Single person, whole task. Candidates are protocol-style clips cut from AEA single-person scripts (script 1/4/5). Gemini 3.1 Pro cut each recording into "one goal + ordered steps" segments; a second Gemini pass re-checked each clip alone. A clip is kept only if: no other person is present, little or no filler activity, steps happen in order, the end state is reached, protocol score ≥ 4, and at least max(4, #steps − 1) steps are visible. 84 → 44 clips.

2. Complete spatial annotations. Every selected recording has the official raw VRS plus all official MPS outputs: closed/open-loop trajectory, online calibration, semi-dense points + observations, eye gaze, and speech.

3. Spatial audio matters. For each clip we ran SRP-PHAT direction-of-arrival on the 7-mic Aria array (mic positions from the device calibration, directions in the CPF head frame). Checked against SAVVY-Bench partner-speech angles on 3 two-person recordings: median error 12°, 69% within 30°. Two measurements:

   • dense: fraction of loud 0.5 s windows whose sound arrives > 45° off the wearer's forward axis (frac_offaxis45) or > 90° (frac_behind90);
   • annotated: number of annotated non-speech sound events arriving > 45° off-axis.

   spatial_audio_score = frac_offaxis45 + 0.5·frac_behind90 + 0.05·n_annotated_offaxis. Top 20 kept, at most 2 clips per recording.

Note: in single-person recordings most sounds are made by the wearer's own hands, so they come from straight ahead. Roughly the top 8 examples have a strong spatial-audio signal (music, a vacuum or broom, doors or drawers to the side or behind). The rest only have 2–11% off-axis sound.

#	example	goal	sec	off-axis ≥45°	behind ≥90°	annotated off-axis events
1	loc3_script1_seq3_rec1__s0	Tidy the couch	40	0.40	0.30	5
2	loc2_script1_seq3_rec1__s1	sweep the living room floor	78	0.48	0.26	1
3	loc2_script5_seq1_rec1__s0	Bring luggage into the bedroom and open it	36	0.42	0.12	1
4	loc1_script1_seq3_rec1__s1	Wind the cord and put away the vacuum cleaner	41	0.20	0.05	2
5	loc4_script1_seq6_rec1__s1	Fetch drinks from the refrigerator	31	0.24	0.12	0
6	loc1_script5_seq3_rec1__s0	Unpack a suitcase	83	0.17	0.14	1
7	loc1_script5_seq3_rec1__s1	Put away a charger	14	0.18	0.09	1
8	loc1_script4_seq5_rec1__s0	serve food and sit at the table	23	0.18	0.06	1
9	loc4_script1_seq1_rec1__s0	do push-ups using handles	19	0.13	0.13	0
10	loc4_script5_seq7_rec1__s1	Update a grocery list	56	0.14	0.12	0
11	loc1_script1_seq6_rec1__s1	Serve a plate of pretzels	46	0.11	0.00	1
12	loc2_script5_seq6_rec1__s0	fold a pile of laundry	78	0.04	0.02	2
13	loc1_script4_seq4_rec1__s2	Season the eggs	44	0.07	0.03	1
14	loc1_script5_seq2_rec1__s1	prepare and start the washing machine	45	0.08	0.00	1
15	loc2_script1_seq6_rec1__s1	Fetch drinks from the fridge and place them on the table	30	0.10	0.05	0
16	loc2_script5_seq2_rec1__s1	Start a load of laundry	28	0.10	0.05	0
17	loc2_script1_seq6_rec1__s0	Portion pretzels into bowls	16	0.08	0.08	0
18	loc3_script5_seq1_rec1__s0	unpack items from a backpack	17	0.08	0.08	0
19	loc2_script5_seq3_rec1__s0	Move toiletries from the suitcase to the bathroom	12	0.11	0.00	0
20	loc2_script1_seq4_rec1__s0	inflate and tie a balloon	78	0.02	0.00	1

Layout

subset_solo20/
  index.json                         # all 20 examples: goal, steps, clip times (video sec + device sec), scores, per-event DOA
  examples/<seg_id>/
    clip.mp4                         # task clip, 456 px, 20 fps, stereo audio (cut from the recording video)
    mic_array_7ch_48k.wav            # same time span, 7-channel Aria mic array from the VRS (float32, peak-normalised)
    meta.json                        # same entry as in index.json
  recordings/<rec>/
    recording.vrs                    # official raw Aria recording (RGB, SLAM cams, eye cams, 7-ch audio, IMU ...)
    video_456px_20fps_stereo.mp4     # full undistorted recording video (from SAVVY-Bench release)
    official/                        # speech.csv, metadata.json, mps/slam/*, mps/eye_gaze/*  (official AEA)
    legacy/                          # narration.json, sound.json, traj_gaze_20fps.json  (SAVVY working files, NOT official)

Time alignment: clip_start_sec / clip_end_sec are seconds into video_456px_20fps_stereo.mp4. clip_*_device_sec are Aria device-time seconds, the same time base as the VRS, MPS and speech.csv (whose timestamps are in ns or µs). Legacy sound.json times are device seconds as well. Provenance: legacy/narration.json contains automatic per-second captions and legacy/sound.json contains sound-event labels; both come from the SAVVY project's working files. The goals and steps are Gemini drafts and have not been verified by a human.

aea_official/

Per-recording files from the official Aria Everyday Activities release, for 50 recordings (loc{L}_script{S}_seq{Q}_rec{R}):

• speech.csv — speech-to-text words with startTime_ns, endTime_ns, written, confidence
• metadata.json — recording metadata
• mps/slam/ — MPS closed/open-loop trajectories, online calibration, semi-dense points, summary
• mps/eye_gaze/ — MPS eye gaze

savvy/

Ground-truth annotation files from SAVVY / SAVVY-Bench (spatial audio-visual QA on AEA, University of Washington NeuroAI Lab: Mingfei Chen, Zijun Cui, Xiulong Liu et al.):

• SAVVY-Bench/data/spatial_avqa/ — benchmark QA (savvy_bench.json, savvy_bench_file.json, gtavmap.json, human_eval_id.json)
• SAVVY/data/test_json/ — test split json incl. GT AV map
• SAVVY/data/vis_gt_keyframes_all/, SAVVY/data/speech_gt_keyframes_all/ — visual / speech GT keyframes per recording
• SAVVY/data/dense_traj/, SAVVY/data/correct_traj/ — trajectories
• LICENSE, README.md — original SAVVY repo license and readme

Audio tracks and model prediction outputs from the SAVVY repo are not included.

License

• aea_official/, subset_solo20/ (VRS, official, video): subject to the Project Aria / Aria Everyday Activities dataset license terms.
• savvy/: BSD Modified License, © University of Washington (see savvy/LICENSE).

Please cite the original works:

@article{lv2024aria,
  title   = {Aria Everyday Activities Dataset},
  author  = {Lv, Zhaoyang and Charron, Nicholas and Moulon, Pierre and others},
  journal = {arXiv preprint arXiv:2402.13349},
  year    = {2024}
}

SAVVY: Chen et al., "SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing", NeurIPS 2025.