ReasonAVEdit-Bench

A 1,200-sample benchmark for instruction-guided joint audio-video editing, built to measure not only the final edit but the multimodal reasoning behind it: which region to change, which sound source to change, and when the change happens.

Every sample ships the source audio-video, a natural-language instruction, and two decodable reasoning ground truths:

• single-frame visual reasoning - the edit-start frame with the target region flattened to gray 127, plus a binary mask of that region;
• full-timeline acoustic reasoning - a waveform the same length as the clip that is silent outside the edit window and, inside it, contains only the source to be replaced.

No edited target video is provided. This is a benchmark: the input is a real clip plus an instruction, and the edited result is what a method under test is expected to produce.

Contents

Path	Count	Description
videos/	1200	Source clip, 25 fps, frames % 8 == 1, sides multiple of 32, H.264 + 16 kHz AAC
audios/	1200	Source audio, 16 kHz mono WAV
masks/	1200	Full-timeline binary mask of the target region
reason_visual/	1200	Single-frame visual reasoning GT (target region = gray 127)
reason_visual_mask/	1200	Binary region at the anchor frame, for Visual IoU
reason_audio/	1200	Full-timeline acoustic reasoning GT, 16 kHz mono WAV
ref_images/	150	Reference image (reference-guided and instance-addition tracks)
annotations/	1200	Per-sample JSON: instruction, windows, geometry, measured evidence
metadata.csv	1	One row per sample
review.html	1	80-window visual inspection page (serve over HTTP)
train_exclude.list, train_exclude_videoids.txt	2	Leakage-control lists for the source corpora

Total 3.3 GB. All media are real files; nothing is a symlink.

Media is shipped as tar archives

On the Hub the seven media folders live under archives/ as one tar each, because 7,150 loose LFS objects trip the Hub's request rate limit. Unpack them next to metadata.csv and the paths in the CSV resolve as written:

huggingface-cli download bigfacing/ReasonAVEdit-Bench --repo-type dataset --local-dir ReasonAVEdit-Bench
cd ReasonAVEdit-Bench && for f in archives/*.tar; do tar -xf "$f"; done

annotations/, metadata.csv and README.md are stored loose so they stay browsable on the Hub. review.html is an 80-window inspection page that expects the extracted layout, so open it after unpacking and serve the folder over HTTP (python3 -m http.server).

Tracks

Five mutually exclusive categories; a source video appears in exactly one sample.

Category	Sub-track	N	What is hard
Where-only 250	W1_same_class_pair	100	Two instances of the same class, comparable size - only position disambiguates
	W2_spatial_reference	80	Instruction refers by left/right/larger/smaller
	W3_small_or_occluded	70	Target covers < 2% of the frame
Which-only 300	H1_overlapping_voices	120	A second voice overlaps the target speaker
	H2_speech_vs_ambient	100	Speech competes with ambient sound at a comparable level
	H3_offscreen_source	80	The source to edit is never visible; visual GT is a null anchor
When-only 300	T1_word_phrase_swap	200	Replace 1-4 words inside a sentence
	T2_sentence_rewrite	100	Rewrite part of a sentence
Combined 250	C1_overlap_local_speech	100	Pick the right voice and the right time span
	C2_ambient_local_speech	50	Same, against ambient interference
	C3_animal_local	40	Intermittent non-speech source
	C4_spatial_add	50	Add an instance at a required position
	C5_complex_remove	10	Remove an instance from a busy scene
Reference 100	R1_person_identity	50	Swap identity to a reference person
	R2_animal_object_look	30	Swap an animal/object to a reference appearance
	R3_ref_guided_add	20	Add the reference subject to an empty scene

Balanced across languages (zh 605 / en 595) and orientation (landscape 952 / portrait 248).

Suggested metrics

Final edit quality applies to all samples (FVD, TV-A, TC, SSIM, FAD, TA-A, LPAPS, AV-A, PEAVS, Sync-C/D, PQ, BG, DeSync). The reasoning GT enables per-category diagnostics:

• Where-only: Visual IoU, BG
• Which-only: Which-Acc, Acoustic Time-Frequency IoU, LPAPS
• When-only: Acoustic Temporal IoU, Video Temporal IoU, DeSync, A/V support offset
• Combined: all three groups

The 1,100 pure-instruction samples are intended for the main ranking; the 100 reference-guided samples are reported separately, since they take an extra conditioning input.

How the annotations were derived

Selection is grounded in measurements, not caption guesswork:

• Instance count and geometry come from connected components of the SAM2 binary mask, so "the dog on the left" is a checkable claim and the visual GT keeps only the named instance.
• Competing sources come from the source/target/residual decomposition shipped with the underlying corpora: level ratio and temporal overlap are measured directly.
• Voice vs non-voice is decided by Whisper's <|nospeech|> posterior. DSP heuristics (voiced-band energy times syllabic modulation) confuse animal calls with speech at a 30-43% false-positive rate, which is exactly the distinction the Which-only track rests on.

Off-screen sources are annotated on the residual track rather than the mask: an object grounded by SAM2 is by construction visible, so a genuinely unseen source lives in "source audio minus visible target". For those samples the acoustic edit target is the residual and the visual reasoning GT is a null anchor.

Loading

import csv, os
root = "."  # or the snapshot path from huggingface_hub.snapshot_download
rows = list(csv.DictReader(open(os.path.join(root, "metadata.csv"))))
r = rows[0]
print(r["sub_track"], r["instruction"])
video   = os.path.join(root, r["video"])              # model input
audio   = os.path.join(root, r["audio"])              # model input
rv_gt   = os.path.join(root, r["reason_visual"])      # single-frame visual reasoning GT
rv_mask = os.path.join(root, r["reason_visual_mask"]) # binary region for IoU
ra_gt   = os.path.join(root, r["reason_audio"])       # full-timeline acoustic reasoning GT

annotations/{id}.json additionally carries the edit window in seconds and frames, the anchor frame index, per-instance geometry, the selected instance index, and the measurements that placed the sample in its sub-track.

Notes and limitations

• Instructions for the SAM-derived tracks are templated from measured evidence (grounded object phrase, component geometry, a donor drawn from a different category, a donor line taken from real in-domain ASR). They are grammatical and specific but not human-written.
• C4/R3 are instance-addition cases: nothing in the source needs replacing, so the acoustic reasoning GT is a silent null anchor and the visual GT is the placement box.
• Clip length follows the ladder 121/105/97/89/81/73/65 frames at 25 fps, picked as the longest rung a source supports; 990 of 1200 samples are the full 121 frames.
• Source material is drawn from internal talking-head, film/drama and sounding-object corpora. Redistribution beyond research use may be restricted by the terms of those sources.