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KlingTeam/Context-as-Memory-Dataset

Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval SIGGRAPH Asia 2025 [Project page] [ArXiv] [Dataset] File Structure To prepare the dataset for use, merge the parts into a single zip file using the following command: cat Context-as-Memory-Dataset_* > Context-as-Memory-Dataset.zip After extracting Context-as-Memory-Dataset.zip, the dataset will be organized as follows: Context-as-Memory-Dataset… See the full description on the dataset page: https://huggingface.co/datasets/KlingTeam/Context-as-Memory-Dataset.

sourceHugging Faceupdated 1y agoView on Hugging Face

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Dataset Card

<div align="center"> <h1>Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval</h1> <h1>SIGGRAPH Asia 2025</h1> <p> <a href="https://context-as-memory.github.io/">[Project page]</a> <a href="https://arxiv.org/pdf/2506.03141">[ArXiv]</a> <a href="https://huggingface.co/datasets/KwaiVGI/Context-as-Memory-Dataset">[Dataset]</a> </p> </div>

File Structure

To prepare the dataset for use, merge the parts into a single zip file using the following command:

bash

cat Context-as-Memory-Dataset_* > Context-as-Memory-Dataset.zip

After extracting Context-as-Memory-Dataset.zip, the dataset will be organized as follows:

Context-as-Memory-Dataset
├── frames
│   ├── AncientTempleEnv_0
│   │   ├── 0000.png
│   │   ├── 0001.png
│   │   ├── 0002.png
│   │   └── ...
│   ├── AncientTempleEnv_1
│   │   ├── 0000.png
│   │   ├── 0001.png
│   │   ├── 0002.png
│   │   └── ...
│   └── ...
│  
├── jsons
│   ├── AncientTempleEnv_0.json
│   ├── AncientTempleEnv_1.json
│   └── ...
│
├── overlap_labels
│   ├── AncientTempleEnv_0
│   │   ├── 0.json
│   │   ├── 1.json
│   │   ├── 2.json
│   │   └── ...
│   ├── AncientTempleEnv_1
│   │   ├── 0.json
│   │   ├── 1.json
│   │   ├── 2.json
│   │   └── ...
│   └── ...
│  
└── captions.txt

Explanation of Dataset Parts

—`frames/`: 100 subdirectories, each containing 7,601 video frame images.
—`jsons/`: 100 JSON files, each storing the camera pose (position + rotation) of every frame in the corresponding long video.
—`overlap_labels/`: 100 subdirectories, each containing 7,601 JSON files, where each file records the indices of overlapping frames corresponding to that frame.
—`captions.txt`: Captions annotated for a segment of a long video, from a given starting frame to an ending frame.
—We also provide a simple code file, tools.py, which can convert (x, y, z, yaw, pitch) into RT, and can also select a specific frame as the reference frame to align the RT of other frames to its coordinate system.