Add task categories and sample usage to dataset card

README.md

@@ -1,199 +1,217 @@
----
-license: mit
-homepage: https://microsoft.github.io/AVGen-Bench/
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: metadata.parquet
----
-
-# AVGen-Bench Generated Videos Data Card
-
-## Overview
-
-This data card describes the generated audio-video outputs stored directly in the repository root by model directory.
-
-The collection is intended for **benchmarking and qualitative/quantitative evaluation** of text-to-audio-video (T2AV) systems. It is not a training dataset. Each item is a model-generated video produced from a prompt defined in `prompts/*.json`.
-
-[![Project Page](https://img.shields.io/badge/Project%20Page-AVGenBench-8dbb3c?style=for-the-badge&labelColor=4c4c4c)](http://aka.ms/avgenbench)
-[![Code Repository](https://img.shields.io/badge/Code%20Repository-GitHub-24292f?style=for-the-badge&logo=github&logoColor=white)](https://github.com/microsoft/AVGen-Bench)
-[![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b?style=for-the-badge&labelColor=4c4c4c)](https://arxiv.org/abs/2604.08540)
-
-For Hugging Face Hub compatibility, the repository includes a root-level `metadata.parquet` file so the Dataset Viewer can expose each video as a structured row with prompt metadata instead of treating the repo as an unindexed file dump.
-The relative video path is stored as a plain string column (`video_path`) rather than a media-typed `file_name` column, which avoids current Dataset Viewer post-processing failures on video rows.
-
-## What This Dataset Contains
-
-The dataset is organized by:
-
-1. Model directory
-2. Video category
-3. Generated `.mp4` files
-
-A typical top-level structure is:
-
-```text
-AVGen-Bench/
-├── Kling_2.6/
-├── LTX-2/
-├── LTX-2.3/
-├── MOVA_360p_Emu3.5/
-├── MOVA_360p_NanoBanana_2/
-├── Ovi_11/
-├── Seedance_1.5_pro/
-├── Sora_2/
-├── Veo_3.1_fast/
-├── Veo_3.1_quality/
-├── Wan_2.2_HunyuanVideo-Foley/
-├── Wan_2.6/
-├── metadata.parquet
-├── prompts/
-└── reference_image/   # optional, depending on generation pipeline
-```
-
-Within each model directory, videos are grouped by category, for example:
-
-```text
-Veo_3.1_fast/
-├── ads/
-├── animals/
-├── asmr/
-├── chemical_reaction/      
-├── cooking/
-├── gameplays/
-├── movie_trailer/
-├── musical_instrument_tutorial/
-├── news/
-├── physical_experiment/
-└── sports/
-```
-
-## Prompt Coverage
-
-Prompt definitions are stored in `prompts/*.json`.
-
-The current prompt set contains **235 prompts** across **11 categories**:
-
-| Category | Prompt count |
-|---|---:|
-| `ads` | 20 |
-| `animals` | 20 |
-| `asmr` | 20 |
-| `chemical_reaction` | 20 |
-| `cooking` | 20 |
-| `gameplays` | 20 |
-| `movie_trailer` | 20 |
-| `musical_instrument_tutorial` | 35 |
-| `news` | 20 |
-| `physical_experiment` | 20 |
-| `sports` | 20 |
-
-Prompt JSON entries typically contain:
-
-- `content`: a short content descriptor used for naming or indexing
-- `prompt`: the full generation prompt
-
-
-## Data Instance Format
-
-Each generated item is typically:
-
-- A single `.mp4` file
-- Containing model-generated video and, when supported by the model/pipeline, synthesized audio
-- Stored under `<model>/<category>/`
-
-The filename is usually derived from prompt content after sanitization. Exact naming may vary by generation script or provider wrapper.
-In the standard export pipeline, the filename is derived from the prompt's `content` field using the following logic:
-
-```python
-def safe_filename(name: str, max_len: int = 180) -> str:
-    name = str(name).strip()
-    name = re.sub(r"[/\\:*?\"<>|\\n\\r\\t]", "_", name)
-    name = re.sub(r"\\s+", " ", name).strip()
-    if not name:
-        name = "untitled"
-    if len(name) > max_len:
-        name = name[:max_len].rstrip()
-    return name
-```
-
-So the expected output path pattern is:
-
-```text
-<model>/<category>/<safe_filename(content)>.mp4
-```
-
-For Dataset Viewer indexing, `metadata.parquet` stores one row per exported video with:
-
-- `video_path`: relative path to the `.mp4` stored as a plain string
-- `model`: model directory name
-- `category`: benchmark category
-- `content`: prompt short name
-- `prompt`: full generation prompt
-- `prompt_id`: index inside `prompts/<category>.json`
-
-## How The Data Was Produced
-
-The videos were generated by running different T2AV systems on a shared benchmark prompt set.
-
-Important properties:
-
-- All systems are evaluated against the same category structure
-- Outputs are model-generated rather than human-recorded
-- Different models may expose different generation settings, resolutions, or conditioning mechanisms
-- Some pipelines may additionally use first-frame or reference-image inputs, depending on the underlying model
-
-## Intended Uses
-
-This dataset is intended for:
-
-- Benchmarking T2AV generation systems
-- Running AVGen-Bench evaluation scripts
-- Comparing failure modes across models
-- Qualitative demo curation
-- Error analysis by category or prompt type
-
-## Out-of-Scope Uses
-
-This dataset is not intended for:
-
-- Training a general-purpose video generation model
-- Treating model outputs as factual evidence of real-world events
-- Safety certification of a model without additional testing
-- Any claim that benchmark performance fully captures downstream deployment quality
-
-## Known Limitations
-
-- Outputs are synthetic and inherit the biases and failure modes of the generating models
-- Some categories emphasize benchmark stress-testing rather than natural real-world frequency
-- File availability may vary across models if a generation job failed, timed out, or was filtered
-- Different model providers enforce different safety and moderation policies; some prompts may be rejected during provider-side review, which can lead to missing videos for specific models even when the prompt exists in the benchmark
-
-
-## Risks and Responsible Use
-
-Because these are generated videos:
-
-- Visual realism does not imply factual correctness
-- Audio may contain artifacts, intelligibility failures, or misleading synchronization
-- Generated content may reflect stereotypes, implausible causal structure, or unsafe outputs inherited from upstream models
-
-Anyone redistributing results should clearly label them as synthetic model outputs.
-
-## Citation
-
-If you find AVGen-Bench useful, please cite:
-
-```bibtex
-@misc{zhou2026avgenbenchtaskdrivenbenchmarkmultigranular,
-      title={AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation}, 
-      author={Ziwei Zhou and Zeyuan Lai and Rui Wang and Yifan Yang and Zhen Xing and Yuqing Yang and Qi Dai and Lili Qiu and Chong Luo},
-      year={2026},
-      eprint={2604.08540},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2604.08540}, 
-}
-```
-
+---
+license: mit
+homepage: https://microsoft.github.io/AVGen-Bench/
+task_categories:
+- text-to-video
+- text-to-audio
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: metadata.parquet
+---
+
+# AVGen-Bench Generated Videos Data Card
+
+## Overview
+
+This data card describes the generated audio-video outputs stored directly in the repository root by model directory.
+
+The collection is intended for **benchmarking and qualitative/quantitative evaluation** of text-to-audio-video (T2AV) systems. It was presented in the paper [AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation](https://arxiv.org/abs/2604.08540). It is not a training dataset. Each item is a model-generated video produced from a prompt defined in `prompts/*.json`.
+
+[![Project Page](https://img.shields.io/badge/Project%20Page-AVGenBench-8dbb3c?style=for-the-badge&labelColor=4c4c4c)](http://aka.ms/avgenbench)
+[![Code Repository](https://img.shields.io/badge/Code%20Repository-GitHub-24292f?style=for-the-badge&logo=github&logoColor=white)](https://github.com/microsoft/AVGen-Bench)
+[![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b?style=for-the-badge&labelColor=4c4c4c)](https://arxiv.org/abs/2604.08540)
+
+For Hugging Face Hub compatibility, the repository includes a root-level `metadata.parquet` file so the Dataset Viewer can expose each video as a structured row with prompt metadata instead of treating the repo as an unindexed file dump.
+The relative video path is stored as a plain string column (`video_path`) rather than a media-typed `file_name` column, which avoids current Dataset Viewer post-processing failures on video rows.
+
+## Sample Usage
+
+As described in the GitHub repository, you can generate videos from the benchmark prompts using the following command:
+
+```bash
+python batch_generate.py \
+  --provider sora2 \
+  --task_type video_generation \
+  --prompts_dir ./prompts \
+  --out_dir ./generated_videos/sora2 \
+  --concurrency 2 \
+  --seconds 12 \
+  --size 1280x720
+```
+
+## What This Dataset Contains
+
+The dataset is organized by:
+
+1. Model directory
+2. Video category
+3. Generated `.mp4` files
+
+A typical top-level structure is:
+
+```text
+AVGen-Bench/
+├── Kling_2.6/
+├── LTX-2/
+├── LTX-2.3/
+├── MOVA_360p_Emu3.5/
+├── MOVA_360p_NanoBanana_2/
+├── Ovi_11/
+├── Seedance_1.5_pro/
+├── Sora_2/
+├── Veo_3.1_fast/
+├── Veo_3.1_quality/
+├── Wan_2.2_HunyuanVideo-Foley/
+├── Wan_2.6/
+├── metadata.parquet
+├── prompts/
+└── reference_image/   # optional, depending on generation pipeline
+```
+
+Within each model directory, videos are grouped by category, for example:
+
+```text
+Veo_3.1_fast/
+├── ads/
+├── animals/
+├── asmr/
+├── chemical_reaction/      
+├── cooking/
+├── gameplays/
+├── movie_trailer/
+├── musical_instrument_tutorial/
+├── news/
+├── physical_experiment/
+└── sports/
+```
+
+## Prompt Coverage
+
+Prompt definitions are stored in `prompts/*.json`.
+
+The current prompt set contains **235 prompts** across **11 categories**:
+
+| Category | Prompt count |
+|---|---:|
+| `ads` | 20 |
+| `animals` | 20 |
+| `asmr` | 20 |
+| `chemical_reaction` | 20 |
+| `cooking` | 20 |
+| `gameplays` | 20 |
+| `movie_trailer` | 20 |
+| `musical_instrument_tutorial` | 35 |
+| `news` | 20 |
+| `physical_experiment` | 20 |
+| `sports` | 20 |
+
+Prompt JSON entries typically contain:
+
+- `content`: a short content descriptor used for naming or indexing
+- `prompt`: the full generation prompt
+
+
+## Data Instance Format
+
+Each generated item is typically:
+
+- A single `.mp4` file
+- Containing model-generated video and, when supported by the model/pipeline, synthesized audio
+- Stored under `<model>/<category>/`
+
+The filename is usually derived from prompt content after sanitization. Exact naming may vary by generation script or provider wrapper.
+In the standard export pipeline, the filename is derived from the prompt's `content` field using the following logic:
+
+```python
+def safe_filename(name: str, max_len: int = 180) -> str:
+    name = str(name).strip()
+    name = re.sub(r"[/\\:*?\"<>|\
+\\r\\t]", "_", name)
+    name = re.sub(r"\\s+", " ", name).strip()
+    if not name:
+        name = "untitled"
+    if len(name) > max_len:
+        name = name[:max_len].rstrip()
+    return name
+```
+
+So the expected output path pattern is:
+
+```text
+<model>/<category>/<safe_filename(content)>.mp4
+```
+
+For Dataset Viewer indexing, `metadata.parquet` stores one row per exported video with:
+
+- `video_path`: relative path to the `.mp4` stored as a plain string
+- `model`: model directory name
+- `category`: benchmark category
+- `content`: prompt short name
+- `prompt`: full generation prompt
+- `prompt_id`: index inside `prompts/<category>.json`
+
+## How The Data Was Produced
+
+The videos were generated by running different T2AV systems on a shared benchmark prompt set.
+
+Important properties:
+
+- All systems are evaluated against the same category structure
+- Outputs are model-generated rather than human-recorded
+- Different models may expose different generation settings, resolutions, or conditioning mechanisms
+- Some pipelines may additionally use first-frame or reference-image inputs, depending on the underlying model
+
+## Intended Uses
+
+This dataset is intended for:
+
+- Benchmarking T2AV generation systems
+- Running AVGen-Bench evaluation scripts
+- Comparing failure modes across models
+- Qualitative demo curation
+- Error analysis by category or prompt type
+
+## Out-of-Scope Uses
+
+This dataset is not intended for:
+
+- Training a general-purpose video generation model
+- Treating model outputs as factual evidence of real-world events
+- Safety certification of a model without additional testing
+- Any claim that benchmark performance fully captures downstream deployment quality
+
+## Known Limitations
+
+- Outputs are synthetic and inherit the biases and failure modes of the generating models
+- Some categories emphasize benchmark stress-testing rather than natural real-world frequency
+- File availability may vary across models if a generation job failed, timed out, or was filtered
+- Different model providers enforce different safety and moderation policies; some prompts may be rejected during provider-side review, which can lead to missing videos for specific models even when the prompt exists in the benchmark
+
+
+## Risks and Responsible Use
+
+Because these are generated videos:
+
+- Visual realism does not imply factual correctness
+- Audio may contain artifacts, intelligibility failures, or misleading synchronization
+- Generated content may reflect stereotypes, implausible causal structure, or unsafe outputs inherited from upstream models
+
+Anyone redistributing results should clearly label them as synthetic model outputs.
+
+## Citation
+
+If you find AVGen-Bench useful, please cite:
+
+```bibtex
+@misc{zhou2026avgenbenchtaskdrivenbenchmarkmultigranular,
+      title={AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation}, 
+      author={Ziwei Zhou and Zeyuan Lai and Rui Wang and Yifan Yang and Zhen Xing and Yuqing Yang and Qi Dai and Lili Qiu and Chong Luo},
+      year={2026},
+      eprint={2604.08540},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2604.08540}, 
+}
+```