README.md

metadata

task_categories:
  - video-generation
  - text-to-video
language:
  - en
tags:
  - video
  - synthetic
  - cinematic
  - panoramic image
pretty_name: Scene-Decoupled Video Dataset
size_categories:
  - 150G<n<200G
arxiv: 2602.06959

CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video Generation

CVPR 2026: Arxiv | Project Page

Scene-Decoupled Video Dataset

TL;DR: The Scene-Decoupled Video Dataset, introduced in CineScene, is a large-scale synthetic dataset for video generation with decoupled scene, which encompasses diverse scenes, subjects, and camera movements. This dataset contains camera trajectories, equirectangular panorama (scene image), and videos with/without dynamic subject. The data is organized into "With Human" (whuman) and "Without Human" (wohuman) categories, while panoramas are scene-decoupled and shared across both.

1. Directory Tree

.
├── camera/                           # Camera trajectories and metadata
│   ├── whuman/                       # Sequences containing human characters
│   │   └── <scene_id>/               # e.g., scene1_3x3_loc1_scene_AncientTempleEnv/
│   │       └── <scene_id>_cam.json   # Camera parameters
│   └── wohuman/                      # Sequences with environment only
│       └── <scene_id>/
│           └── <scene_id>_cam.json
│
├── panorama/                         # Scene-decoupled environment maps
│   └── <scene_id>/                   # Shared between whuman and wohuman
│       └── <scene_id>_pano.jpeg      # 360° Equirectangular panoramic image
│
└── video/                            # Rendered video sequences (MP4)
    ├── whuman/                       # Videos with human characters
    │   └── <scene_id>/
    │       ├── <scene_id>_01_24mm.mp4 # Sub-sequences (01, 02, etc.)
    │       ├── <scene_id>_02_24mm.mp4
    │       └── ...
    └── wohuman/                      # Videos without human characters
        └── <scene_id>/
            ├── <scene_id>_01_24mm.mp4
            ├── ...

2. Dataset Statistics

• Total Scale: 46,816 videos.
• Scenes: 3,400 scenes (comprising both whuman and wohuman scenes) across 35 high-quality 3D environments.
• Trajectories: 46,816 camera paths (7 distinct camera trajectories per scene).
• Panorama: 360° Equirectangular images for every scene, providing a complete background reference for scene conditioning.

Property	Value
Video Resolution	672 x 384
Frame Count	81 frames per video
Frame Rate	15 FPS
View Change Range	Up to 75°
Decoupled Scene	360° Equirectangular (Panorama)
Panorama Resolution	2048 x 1024

3. Dataset Construction

We follow the asset collection pipeline established by RecamMaster, but introduce three significant enhancements to support more complex generative tasks:

1. Decoupled Scenes: We provide static 360° panoramic images (Equirectangular) for every scene. This allows for explicit background conditioning and facilitates novel view synthesis from any angle.
2. Extended Camera Range: Our dataset covers significantly larger view changes (approx. 75°) compared to the 5–60° range provided in previous datasets.
3. Paired Subject/Background Data: Every scene includes both "with-subject" (whuman) and "background-only" (wohuman) video sequences. This paired data is ideal for training models on subject-background decoupling, motion transfer, and cinematic composition.

4. useful script

• download

sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/datasets/KlingTeam/Scene-Decoupled-Video-Dataset
cat Scene-Decoupled-Video-Dataset.part* > Scene-Decoupled-Video-Dataset.tar.gz
tar -xvf Scene-Decoupled-Video-Dataset.tar.gz

• camera visualization

  To visualize the camera, please refer to here.

• Perspective Projection To extract perspective frames from the panoramic images:

  python extract_scene_from_panorama.py