Xinpeng-Li/Omni_MMSI

Omni-MMSI: Toward Identity-attributed Social Interaction Understanding

Webpage | arXiv | YouTube | Github

Xinpeng Li, Bolin Lai, Hardy Chen, Shijian Deng, Cihang Xie, Yuyin Zhou, James Matthew Rehg, Yapeng Tian

Introduction

Omni-MMSI is an annotation dataset for identity-attributed social interaction understanding in multi-party social deduction game videos. It provides instruction-formatted metadata for clips, including labels, oracle cues, extracted cues, identity references, and training-only chain-of-thought reasoning traces.

The dataset covers two referent prediction tasks:

• Speaking Target Identification (STI): identify which player the last speaker refers to with second-person pronouns such as "you" or "your".
• Pronoun Coreference Resolution (PCR): identify which player the last speaker refers to with third-person pronouns such as "he", "him", "his", "she", or "her".

Each example contains prompt fields, media path hints, cue annotations, and a ground_truth player label such as Player0, Player1, or Player3. The ora_* fields provide oracle/reference cues, while the raw_* fields provide extracted cues from the raw input. wref files include paired audio/image reference paths for player identity attribution, woref files remove references to simulate non-reference settings, and COT files include Gemini-generated reasoning traces for training.

Data Organization

The metadata files are organized by task, source, split, and prompting setting:

omni_mmsi_pcr_ego4d_video_turn5/
  train/
  test/
omni_mmsi_pcr_youtube_video_turn5/
  train/
  test/
omni_mmsi_sti_ego4d_video_turn5/
  train/
  test/
omni_mmsi_sti_youtube_video_turn5/
  train/
  test/
reference/
  ego4d_ref_mini.tar.gz
  youtube_ref_mini.tar.gz
  Ego4D_ref_large.tar.gz
  Youtube_ref_large.tar.gz
sample.json

File naming convention:

• metadata_wref_answer_*.json: answer-format examples with reference player image/audio metadata.
• metadata_woref_answer_*.json: answer-format examples without identifiable player references.
• metadata_wref_cot_*_gemini.json: train-only examples with Gemini-generated chain-of-thought reasoning traces.
• reference/*_ref_mini.tar.gz: compressed mini reference image-audio files for player identity attribution.
• reference/*_ref_large.tar.gz: compressed reference video-audio files for better identity attribution.

Dataset Statistics

PCR Source Setting Train Test Ego4D wref answer 387 112 Ego4D woref answer 387 112 Ego4D wref COT 387 - YouTube wref answer 2,155 521 YouTube woref answer 2,155 521 YouTube wref COT 2,155 -

STI Source Setting Train Test Ego4D wref answer 645 175 Ego4D woref answer 645 175 Ego4D wref COT 645 - YouTube wref answer 2,593 654 YouTube woref answer 2,593 654 YouTube wref COT 2,593 -

Total rows across all metadata JSON files: 20,264.

Metadata Fields

Common fields:

• system: task instruction prompt.
• messages: chat-style user message list.
• user_content: formatted user prompt content.
• ground_truth: target player label.
• prediction: parsed model prediction when available.
• explanation: explanation or reviewed explanation when available.
• raw_response: original model response text.
• videos: path hints for query video files. The video name is formatted as [source_video]_[unique_id]_[start_time]_[end_time].mp4.
• audios: path hints for query and/or reference audio files. Same naming format as video.
• images: path hints for reference player images, present in with-reference files.
• ora_transcript: oracle transcript.
• raw_transcript: extracted transcript from the raw audio input.
• ora_bbox: oracle player bounding boxes.
• raw_bbox: extracted bounding box cues from the raw video input.

Additional COT fields:

• samples: sampled candidate reasoning/answer responses from the generation and filtering process.
• is_correct: whether the reviewed response matches ground_truth.
• error: error note, if any.
• elapsed_seconds: processing time for the response generation/review step.

More about naming:

The query media paths in videos and the query .mp3 entries in audios use the following basename format:

[source_video]_[unique_id]_[start_time]_[end_time].mp4
[source_video]_[unique_id]_[start_time]_[end_time].mp3

• source_video: upstream Werewolf source video name.
• unique_id: example identifier used only to keep generated clip filenames unique.
• start_time and end_time: clip boundaries in seconds in the source video.

For example, 0d495a56-676b-4bb0-bb7d-9abf88fb7beb_Game3_3_138_0_137_169.mp4 is generated from the Werewolf source video 0d495a56-676b-4bb0-bb7d-9abf88fb7beb_Game3_3.mp4 by cutting the segment from 137 to 169 seconds. The unique_id part, 138_0 in this example, is not needed to locate or cut the source video; it only needs to be preserved in the output filename.

Data Usage

To use the multimodal inputs, download the Omni-MMSI files and obtain the source videos from Werewolf Among Us. Recommended workflow:

1. Unpack reference/ego4d_ref_mini.tar.gz and/or reference/youtube_ref_mini.tar.gz for mini reference image/audio files. If reference videos are needed, also unpack reference/Ego4D_ref_large.tar.gz and/or reference/Youtube_ref_large.tar.gz.
2. Download the upstream Werewolf source videos and generate query .mp4/.mp3 clips by cutting each source video according to the start_time and end_time encoded in the target media basename.
3. Remap videos, audios, and images to your local media root.
4. Use raw_response as the response label for training.
5. Use ground_truth as the answer label for evaluation.

BibTex

Please cite the OmniMMSI paper if this dataset is helpful to your research:

@inproceedings{li2026omni,
  title={Omni-MMSI: Towards Identity-attributed Social Interaction Understanding},
  author={Li, Xinpeng and Lai, Bolin and Chen, Hardy and Deng, Shijian and Xie, Cihang and Zhou, Yuyin and Rehg, James M and Tian, Yapeng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}