diff --git a/README.md b/README.md index 7b95401dc46245ac339fc25059d4a56d90b4cde5..349deb203c3147157587f10e9815e615c86c5a16 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,230 @@ ---- -license: apache-2.0 ---- +--- +license: apache-2.0 + +dataset_info: + features: + train: + - name: video + dtype: binary + - name: internal_id + dtype: string + - name: url + dtype: string + - name: annotation + struct: + - name: text_fit + dtype: int64 + - name: composition_focus + dtype: int64 + - name: focus + dtype: int64 + - name: camera_movement_quality + dtype: int64 + - name: color + dtype: int64 + - name: shadow_correct + dtype: int64 + - name: shadow_goodlooking + dtype: int64 + - name: morphology_build + dtype: int64 + - name: morphology_maintain + dtype: int64 + - name: object_movement + dtype: int64 + - name: camera_movement + dtype: int64 + - name: movement_smoothing + dtype: int64 + - name: movement_reality + dtype: int64 + - name: clarity + dtype: int64 + - name: quality + dtype: int64 + - name: stability + dtype: int64 + - name: detail + dtype: int64 + - name: letter + dtype: int64 + - name: physical_reality + dtype: int64 + - name: harmful + dtype: int64 + - name: harmful_degree + dtype: int64 + - name: meta_result + dtype: sequence[int64] + - name: meta_mask + dtype: sequence[int64] + + regression: + - name: internal_id + dtype: string + - name: prompt + dtype: string + - name: standard_answer + dtype: string + - name: file1 + dtype: binary + - name: file2 + dtype: binary + + monetbench: + - name: internal_id + dtype: string + - name: prompt + dtype: string + - name: standard_answer + dtype: string + - name: file1 + dtype: binary + - name: file2 + dtype: binary + + splits: + - name: train + num_examples: 40743 + - name: regression + num_examples: 2260 + - name: monetbench + num_examples: 1000 +--- + +# VRDB-Video + +This dataset is a comprehensive collection of video evaluation data designed for multi-dimensional quality assessment of AI-generated videos. It encompasses annotations across 21 diverse aspects, including text-to-video consistency, aesthetic quality, motion dynamics, physical realism, and technical specifications. + +The dataset is structured to facilitate both model training and standardized evaluation: +- `Train`: A primary training set with detailed multi-dimensional annotations +- `Regression`: A regression set with paired preference data +- `MonetBench`: A benchmark test set for standardized performance evaluation + +This holistic approach enables the development and validation of sophisticated video quality assessment models that can evaluate AI-generated videos across multiple critical dimensions, moving beyond simple aesthetic judgments to encompass technical accuracy, semantic consistency, and dynamic performance. + + +## Annotation Details + +Each video in the dataset is annotated with the following attributes: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DimensionSub-dimension
AlignmentText Fit
Text Accuracy
CompositionComposition Quality
Focus Effect
Camera Movement Artistry
QualityColor Harmony
Shadow Technical Accuracy
Shadow Aesthetic Effect
PreservationInitial Object Form
Form Consistency
DynamicObject Motion Level
Camera Motion Level
Motion Fluidity
StabilityVisual Clarity
Image Stability
Camera Stability
Motion Naturality
FidelityDetail Refinement
Physical Realism
SafetyContent Safety Type
Safety Level
+ +### Example: Camera Stability (cam_stable) + - **3:** Very stable + - **2:** Slight shake + - **1:** Heavy shake +- Note: When annotations are missing, the corresponding value will be set to **-1**. + +For more detailed annotation guidelines, please refer to: + +- [annotation_deatils](https://www.notion.so/VisionReward-Video-Annotation-Details-196a0162280e80768c89f6401fbb530c?pvs=4) +- [annotation_deatils_ch](https://www.notion.so/VisionReward-Video-196a0162280e80e7806af42fc5808c99?pvs=4) + +## Additional Feature Details +The dataset includes two special features: `annotation` and `meta_result`. + +### Annotation +The `annotation` feature contains scores across 21 different dimensions of image assessment, with each dimension having its own scoring criteria as detailed above. + +### Meta Result +The `meta_result` feature transforms multi-choice questions into a series of binary judgments. For example, for the `Camera Stability` dimension: + +| Score | Is the camera very stable? | Is the camera not unstable? | +|-------|--------------------------|---------------------------| +| 3 | 1 | 1 | +| 2 | 0 | 1 | +| 1 | 0 | 0 | + +- note: When the corresponding meta_result is -1 (It means missing annotation), the binary judgment should be excluded from consideration + +Each element in the binary array represents a yes/no answer to a specific aspect of the assessment. For detailed questions corresponding to these binary judgments, please refer to the meta_qa_en.txt file. + +### Meta Mask +The `meta_mask` feature is used for balanced sampling during model training: +- Elements with value 1 indicate that the corresponding binary judgment was used in training +- Elements with value 0 indicate that the corresponding binary judgment was ignored during training + +## Data Processing + +We provide `extract.py` for processing the `train` dataset into JSONL format. The script can optionally extract the balanced positive/negative QA pairs used in VisionReward training by processing `meta_result` and `meta_mask` fields. + +```bash +python extract.py [--save_imgs] [--process_qa] +``` \ No newline at end of file diff --git a/extract.py b/extract.py new file mode 100644 index 0000000000000000000000000000000000000000..339d768d237139ab4af7e95278da9fd10a120c75 --- /dev/null +++ b/extract.py @@ -0,0 +1,107 @@ +import json +import os +import logging +import argparse +from datasets import Dataset +import io + +# Configure logging for detailed output +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +def load_questions_from_meta_qa(meta_qa_file): + with open(meta_qa_file, "r") as f: + questions = [line.strip() for line in f if line.strip()] + return questions + +def process_parquet_files(data_dir, output_jsonl, meta_qa_file=None, output_videos=None, process_qa=False): + """ + Process Parquet files to generate a JSONL file with optional video export and QA list creation. + + Args: + data_dir (str): Directory containing Parquet files. + output_jsonl (str): Output JSONL file path. + meta_qa_file (str, optional): Path to the meta_qa_en.txt file for QA list creation. + output_videos (str, optional): Directory path to save videos. If None, videos are not saved. + process_qa (bool): Whether to process and include QA pairs in the output. + + Returns: + None + """ + + if output_videos and not os.path.exists(output_videos): + os.makedirs(output_videos) + + # Load questions only if QA processing is enabled + questions = None + if process_qa and meta_qa_file: + questions = load_questions_from_meta_qa(meta_qa_file) + + jsonl_data = [] + + parquet_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(".parquet")] + + for parquet_file in parquet_files: + dataset = Dataset.from_parquet(parquet_file) + + for row in dataset: + json_item = { + "internal_id": row["internal_id"], + "url": row["url"], + "prompt":row["prompt"], + "annotation": row["annotation"], + "meta_result": row["meta_result"], + "meta_mask": row["meta_mask"], + } + + # Optionally save videos + if output_videos: + video_data = row["video"] + video_path = os.path.join(output_videos, f"{row['internal_id']}.mp4") + + try: + with open(video_path, "wb") as video_file: + video_file.write(video_data) + json_item["video_path"] = video_path + except Exception as e: + logger.error(f"Error saving video for internal_id {row['internal_id']}: {e}") + + # Optionally process QA pairs + if process_qa and questions: + qa_list = [] + meta_result = row["meta_result"] + meta_mask = row["meta_mask"] + for idx, mask in enumerate(meta_mask): + if mask == 1: # Add questions only if the mask is 1 + question = questions[idx] + if "[[prompt]]" in question: + question = question.replace("[[prompt]]", row["prompt"]) + answer = 'yes' if meta_result[idx] == 1 else 'no' + qa_list.append({"question": question, "answer": answer}) + json_item["qa_list"] = qa_list + + jsonl_data.append(json_item) + + with open(output_jsonl, "w") as outfile: + for json_item in jsonl_data: + outfile.write(json.dumps(json_item) + "\n") + logger.info(f"Finished writing JSONL file with {len(jsonl_data)} items.") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Convert Video dataset Parquet files to JSONL format with optional video extraction and QA list generation.") + parser.add_argument("--data_dir", type=str, default='train', help="Directory containing Parquet files.") + parser.add_argument("--output_jsonl", type=str, default='annotation.jsonl', help="Path to the output JSONL file.") + parser.add_argument("--meta_qa_file", type=str, default="meta_qa_en.txt", help="Optional: Path to the meta_qa_en.txt file for QA list generation.") + parser.add_argument("--save_videos", action="store_true", help="Optional: Whether to save videos.") + parser.add_argument("--process_qa", action="store_true", help="Optional: Process and include QA pairs in the output.") + args = parser.parse_args() + + output_videos = 'videos' if args.save_videos else None + + process_parquet_files( + data_dir=args.data_dir, + output_jsonl=args.output_jsonl, + meta_qa_file=args.meta_qa_file, + output_videos=output_videos, + process_qa=args.process_qa + ) \ No newline at end of file diff --git a/meta_qa_en.txt b/meta_qa_en.txt new file mode 100644 index 0000000000000000000000000000000000000000..14e18f47713a6358400398b8eab164c0e10fa011 --- /dev/null +++ b/meta_qa_en.txt @@ -0,0 +1,64 @@ +Does the video meet all the requirements stated in the text "[[prompt]]"? +Does the video meet most of the requirements stated in the text "[[prompt]]"? +Does the video meet some of the requirements stated in the text "[[prompt]]"? +Does the video not completely fail to meet the requirements stated in the text "[[prompt]]"? +Is the composition aesthetically pleasing? +Does the composition have no obvious flaws? +Is the focus aesthetically pleasing? +Does the focus have no obvious flaws? +Is the camera movement aesthetically pleasing? +Does the camera movement have no obvious flaws? +Are the colors exceptionally beautiful? +Are the colors beautiful? +Are the colors not unattractive? +Are the colors not significantly unattractive? +Is the lighting perfectly accurate? +Does the lighting have no obvious errors? +Is there any lighting present? +Is the lighting exceptionally beautiful? +Is the lighting beautiful? +Is the lighting not unattractive? +Is the shape of the object at the beginning of the video completely accurate? +Does the shape of the object at the beginning have no obvious errors? +Is the shape of the object at the beginning not chaotic? +Is the shape of the object perfectly maintained throughout the video? +Does the shape of the object have no obvious issues throughout the video? +Does the shape of the object generally have no major issues throughout the video? +Is the shape of the object not chaotic throughout the video? +Is the object's motion highly dynamic? +Is the object's motion dynamic? +Is the object's motion not minimal? +Is the object's motion not static? +Is the camera motion highly dynamic? +Is the camera motion dynamic? +Is the camera motion not minimal? +Is the camera motion not static? +Is the smoothness of the object's movement very good? +Does the smoothness of the object's movement have no obvious issues? +Is the object's movement completely realistic? +Does the object's movement have no obvious realism issues? +Is it very clear? +Is it clear? +Is it not blurry? +Is it not completely blurry? +Is the image quality very stable? +Is the image quality stable? +Is the image quality not unstable? +Is the image quality free of noticeable instability? +Is the camera very stable? +Is the camera not unstable? +Are the details very refined? +Are the details relatively refined? +Are the details not rough? +Are the details not significantly rough? +Are all the letters correct? +Do the letters have no obvious errors? +Are there any letters present? +Does it fully comply with the laws of physics? +Does it partially comply with the laws of physics? +Does it have no obvious violations of the laws of physics? +Is the video content part of the physical world? +Is 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