--- configs: - config_name: specs default: true data_files: - split: full path: viewer_data/specs.parquet - config_name: checklists data_files: - split: full path: viewer_data/checklists.parquet - config_name: qa_items data_files: - split: full path: viewer_data/qa_items.parquet - config_name: video_sources data_files: - split: full path: viewer_data/video_sources.parquet - config_name: source_annotations data_files: - split: full path: viewer_data/source_annotations.parquet - config_name: rules data_files: - split: full path: viewer_data/rules.parquet ---

LabInstruct logo

LabInstruct: Benchmarking Situated Instructional Video Generation for Lab Procedures

🌐 [Homepage](https://csu-jpg.github.io/LabInstruct.github.io/) | 👉 [Dataset](#data-files) | 📄 Paper (coming soon) | đŸ’ģ [Code](https://github.com/CSU-JPG/LabInstruct) | 🏆 [Leaderboard](#leaderboard)
> ℹī¸ **This repository only hosts the LabInstruct annotation data**: structured annotations and source-video links only. Videos, clips, and first frames are not included here; rebuild them with the LabInstruct [code repository](https://github.com/CSU-JPG/LabInstruct). ## đŸ“ĸ Updates - **[2026-09]** LabInstruct is online! ## 📝 TODO - [x] Release the annotation data: 204 task specifications, 204 QA checklists, 81 source clip-boundary annotations, and source-video links. - [x] Release the code: data preparation, video generation, and evaluation harness. ## 📑 Table of Contents - [📜 Abstract](#abstract) - [🌟 Project Overview](#project-overview) - [🚀 Setup](#setup) - [🗂 Repository Structure](#repository-structure) - [📁 Data Files](#data-files) - [🏆 Leaderboard](#leaderboard) - [🎓 BibTeX](#bibtex) - [📧 Contact](#contact) - [🙏 Acknowledgements](#acknowledgements) ## 📜 Abstract Self-driving laboratories (SDLs) aim to automate the full experimental loop, from scientific decision-making to physical execution. Ideally, AI-generated plans could be carried out directly by robotic systems, but reliable automation remains difficult in complex, open-world laboratory environments, where experiments often involve fine-grained manipulation, long-horizon procedures, and substantial variation across tasks and setups. Humans therefore remain an important execution interface between AI-generated plans and physical experiments, creating a need for clear and effective human-facing experimental guidance. Because laboratory procedures are inherently visual, spatial, and dynamic, video is particularly well suited to communicating apparatus configurations, manipulation actions, temporal dependencies, and state changes. Recent advances in video generation now make it possible to synthesize experimental demonstrations directly from an initial workspace image and a natural-language instruction. However, whether such models can reliably communicate real laboratory procedures has not been systematically studied. We introduce **LabInstruct**, a benchmark for situated instructional video generation in real laboratories. LabInstruct contains 204 tasks across 5 scientific disciplines, with real reference executions and structured annotations of objects, actions, contacts, and state transitions. Evaluating 8 frontier image-to-video models, we find that visually plausible generations frequently remain procedurally incorrect, revealing a substantial gap between visual realism and the reliability required for experimental instruction. ## 🌟 Project Overview

Overview of the LabInstruct benchmark

Figure 1. LabInstruct evaluates generated laboratory videos for procedural correctness beyond visual plausibility.

## 🚀 Setup > ℹī¸ The setup and usage below refer to the LabInstruct **code repository**. This repository contains only the files under `data/` (see [Data Files](#data-files)), the README figures, and the license; `bench/` and `scripts/` live in the code repository. ### 1. Environment setup Python 3.10 or newer is required. Install `ffmpeg` and `ffprobe` for media reconstruction and video evaluation. ```bash python -m venv .venv source .venv/bin/activate pip install -e . ``` Optional dependencies: ```bash pip install -e ".[diffusers]" pip install -e ".[qa]" ``` ### 2. Prepare benchmark data Place legally obtained source videos under `data/source_videos/`, using the `source_video_id` names in `data/video_sources.csv`. ```bash python scripts/prepare_data.py --check python scripts/prepare_data.py --clips --first-frames python scripts/specs_to_tasks.py ``` ### 3. Generate videos Configure model environments and checkpoints in `bench/models.yaml`, then run: ```bash python -m bench.cli gen \ --run exp1 \ --models wan2.2,ltx2.3,minimax-h3 \ --gpus 0,1,2,3 ``` ### 4. Evaluate videos ```bash export JUDGE_API_BASE_URL="https://your-endpoint.example/v1" export GPT_API_TOKEN="your-token" python scripts/judge_videos_gpt.py --model-name all --fps 4 ``` ## 🗂 Repository Structure ```text . ├── data/ │ ├── video_sources.csv source-video provenance and URLs │ ├── specs/ structured task specifications (204 tasks) │ ├── checklists/ frozen QA checklists (204 tasks) │ ├── source_annotations/ source clip boundaries (81 source videos) │ └── rules/ annotation and evaluation prompts ├── assets/ figures used by this README └── LICENSE Apache License 2.0 ``` > [!IMPORTANT] > This repository releases **links and annotations only**. The third-party source videos, extracted clips, and first frames are **not** included. Download the source videos from the URLs in `data/video_sources.csv` under their original terms, then rebuild clips and first frames with `scripts/prepare_data.py` in the LabInstruct code repository. ## 📁 Data Files This section explains what every file under `data/` is for. All annotation files are UTF-8 JSON or plain text; `data/video_sources.csv` is the only file that references the source videos. ### `data/video_sources.csv` — source-video provenance One row per source video (81 in total: 61 bilibili, 15 FineBio, 3 ExpVid, 2 YouTube). Columns: | Column | Description | | --- | --- | | `source_video_id` | 3-digit source video id (e.g. `001`). Matches the annotation file in `data/source_annotations/` and the `source_video_id` names used by `scripts/prepare_data.py` in the code repository. | | `source_url` | Link to the original video. Use it to download the source video legally under its original terms; the video itself is **not** in this repository. | | `platform` | Hosting platform / source dataset of the video. | | `n_task_clips` | Number of task clips cut from this source video (sums to 204 across all rows). | | `note` | Free-form notes. | ### `data/specs/` — task specifications (204 JSON files) One file per benchmark task, named `{index}_{split}_{source_id}_{discipline}_spec.json` (e.g. `001_L1_001_agronomy_spec.json`). These are the core structured annotations of each laboratory procedure: | Field | Description | | --- | --- | | `task_id` | Unique task id, identical to the file stem. | | `split` | Task level: `L1` (101 tasks) = a single atomic step (1–3 closely related actions); `L2` (103 tasks) = a complete short subprocedure with an ordered action sequence. | | `domain` | Procedure domain, one or more of `solid_handling`, `liquid_handling`, `measurement`, `instrument_operations`, `container_operations`, `heating_cooling`, `filtration_separation` (multiple values joined by `|`). | | `discipline` | Scientific discipline: `agronomy`, `biology`, `chemistry`, `materials_science`, or `physics`. | | `title` / `description` | Short title and a plain-language description of the procedure. | | `initial_image` | Relative path to the task's first frame, which serves as the image input for video generation. The frame itself is **not** included; rebuild it with `prepare_data.py --first-frames` in the code repository. | | `scene` | Annotated scene: `objects` (each with `id`, `name`, `contents`, `material`), `spatial_relations` (natural-language spatial descriptions), and `environment` (e.g. `indoor_laboratory`). | | `action_sequence` | Ordered action steps; each step has `action`, `target_object`, `contact` (e.g. `hand_tool`, `tool_object`), and `state_transition` (`from` / `to` states). | | `prompt_for_gen` | The natural-language instruction used as the text prompt for video generation. | | `difficulty_features` | Quantitative difficulty flags: `number_of_objects`, `number_of_action_steps`, and boolean requirements (`requires_liquid_handling`, `requires_tool_manipulation`, `requires_fine_motor_control`, `requires_transparent_object_reasoning`, `requires_color_tracking`, `requires_volume_change_detection`, `requires_spatial_memory`, `requires_sequential_ordering`). | | `viewpoint` | Recorded viewpoint: `first_person` (67 tasks) or `nearby_observer` (137 tasks). | ### `data/checklists/` — QA checklists (204 JSON files) One file per task, named `{task_id}_qa.json`. Each checklist is a frozen set of yes/no questions for judging whether a *generated* video executes the task correctly. The evaluation prompt `data/rules/vlm_judge.txt` consumes these checklists, and the same questions are what raters answered in the human study. | Field | Description | | --- | --- | | `task_id` / `task_level` / `source_spec` | The task this checklist belongs to and the spec file it was generated from. | | `items[].qa_id` | Question id within the checklist. | | `items[].dimension` | Evaluation dimension, one of six: `Action Fidelity`, `Object Consistency`, `State Correctness`, `Physical Plausibility`, `Visual Safety`, `Scene Consistency`. | | `items[].importance` | Question weight: `critical` or `standard`. | | `items[].question` / `items[].question_zh` | The question in English and in Chinese. | Across the 204 checklists there are 3,229 questions in total: 1,299 Action Fidelity, 567 Object Consistency, 448 State Correctness, 371 Physical Plausibility, 305 Visual Safety, and 239 Scene Consistency. ### `data/source_annotations/` — source clip boundaries (81 CSV files) One file per source video, named `{source_video_id}.txt`. Columns: `start_sec`, `end_sec`, `level`. Each row marks one task clip inside the source video: its start/end time in seconds and the task level (`L1`/`L2`) of that clip. `scripts/prepare_data.py --clips` in the code repository uses these boundaries to cut `data/video_clips/` from the downloaded source videos. The clips themselves are **not** included. ### `data/rules/` — annotation and evaluation prompts (4 files) Plain-text prompts that reproduce the annotation and evaluation pipeline: | File | Purpose | | --- | --- | | `L1_vlm_annotation.txt` | VLM prompt for drafting an L1 spec (atomic-action annotation) from a video clip. | | `L2_vlm_annotation.txt` | VLM prompt for drafting an L2 spec (short-subprocedure annotation) from a video clip. | | `qa_generation.txt` | Prompt for generating the task-specific QA checklist from a spec. | | `vlm_judge.txt` | Prompt for judging a generated video against the frozen checklist. | These prompts are written to produce structured drafts for human verification. ## 🏆 Leaderboard Pooled Overall is evaluated by GPT-5.6 Sol at 4 FPS. Higher is better; † marks commercial models. | Rank | Model | Type | Organization | Pooled Overall | | :--: | :-- | :-- | :-- | --: | | **1** | **MiniMax H3** | Open weight | MiniMax | **46.3** | | 2 | Seedance 2.0 † | Commercial | ByteDance | 45.1 | | 3 | Wan 3.0 † | Commercial | Alibaba | 44.4 | | 4 | Wan 2.2-I2V-A14B | Open weight | Alibaba | 25.9 | | 5 | Cosmos3 Super | Open weight | NVIDIA | 22.8 | | 6 | LTX 2.3 | Open weight | Lightricks | 22.1 | | 7 | LingBot Video | Open weight | Robbyant | 19.5 | | 8 | Cosmos3 Nano | Open weight | NVIDIA | 19.4 | ## 🎓 BibTeX If you find our work helpful, please consider citing it: ```bibtex @misc{fu2026labinstruct, title = {LabInstruct: Benchmarking Situated Instructional Video Generation for Lab Procedures}, author = {Yuming Fu and Weijia Wu and Jing Chen and Jiahao Tang and Feifei Chen and Hongyu Zhu and Xin Jin and Alex Jinpeng Wang}, year = {2026}, eprint = {XXXX.XXXXX}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/XXXX.XXXXX} ``` ## 📧 Contact For questions, please open an issue in this repository or email Yuming Fu at [yumingfu@csu.edu.cn](mailto:yumingfu@csu.edu.cn). ## 🙏 Acknowledgements We thank the creators of the source videos and the authors of FineBio, ExpVid, and the evaluated video-generation models for their work.