DanceNet3D

A large-scale synchronized multi-view dance dataset captured with 28-29 calibrated cameras at 800x1280 resolution. The dataset contains 46 dance sequences across 3 recording sessions, totaling 34,698 frames with per-frame camera calibration (COLMAP), foreground masks, optional color correction LUTs, and per-frame sparse point cloud reconstructions.

Dataset Summary

Session	Sequences (available / total)	Cameras	Frames (available / total)	Size
s4	10 / 10	29	7,723 / 7,723	~33 GB
s5	20 / 23	29	15,401 / 17,928	~64 GB
s6	14 / 14	28	10,668 / 10,668	~37 GB
Total	44 / 46		34,698 / 36,319	~134 GB

Data Format

Each session is stored as a directory (s4/, s5/, s6/) containing per-sequence subdirectories. Videos are encoded per-camera using H.265 (libx265), CRF 18, yuv444p at 1 fps. Each sequence includes:

• Per-camera videos: {SequenceName}_{CameraID}.mp4 undistorted images without color LUT applied
• COLMAP calibration: colmap/cameras.txt, colmap/images.txt and binary formats
• Foreground masks: masks.tar.zst binary person segmentation masks generated with SAM3 and with manual quality review
• Manifest: manifest.json frame IDs, camera lists, and sequence metadata
• Point clouds: pointcloud/{session}/{SequenceName}.tar.zst per-frame sparse point clouds ({FrameID}.ply)

Directory Structure

DanceNet3D/
├── README.md
├── LICENSE
├── requirements.txt
├── video_to_images.py          # Extraction script
├── color_lut/                  # Per-camera color correction LUTs (.cube)
│   ├── 0028.cube
│   └── ...
├── s4/
│   ├── manifest.json
│   └── ...
├── s5/
│   ├── manifest.json
│   ├── AttitudePromenade/
│   │   ├── AttitudePromenade_0028.mp4
│   │   ├── AttitudePromenade_0103.mp4
│   │   ├── ...
│   │   ├── colmap/
│   │   │   ├── cameras.txt
│   │   │   ├── cameras.bin
│   │   │   ├── images.txt
│   │   │   └── images.bin
│   │   └── masks.tar.zst
│   └── ...
├── s6/
│   └── ...
└── pointcloud/
    ├── s4/
    │   └── HouseFootwork.tar.zst
    ├── s5/
    │   ├── BourreeTurns.tar.zst
    │   └── ...
    └── s6/
        ├── BiancaGolden_DropTurn.tar.zst
        └── ...

Extracted Frame Structure

After running video_to_images.py, the data is organized per-frame:

output/
└── AttitudePromenade/
    └── images_and_masks/
        ├── 0000001/
        │   ├── images_no_lut/       # Undistorted images (no color correction)
        │   │   ├── 0028.png
        │   │   ├── 0103.png
        │   │   └── ...
        │   ├── images/              # Color-corrected images (present if --apply-lut used)
        │   │   └── ...
        │   ├── masks/               # Binary foreground masks
        │   │   ├── 0028.png
        │   │   └── ...
        │   └── sparse/0/            # COLMAP calibration
        │       ├── cameras.txt
        │       ├── cameras.bin
        │       ├── images.txt
        │       └── images.bin
        ├── 0000002/
        └── ...

Quick Start

Download

# Install Hugging Face CLI
# macOS/Linux
curl -LsSf https://hf.co/cli/install.sh | bash
# Windows
powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"

# Download entire dataset
hf download nyuvideolab/danceNet3D --repo-type dataset --local-dir DanceNet3D

# Download a specific session
hf download nyuvideolab/danceNet3D --repo-type dataset --local-dir DanceNet3D --include "s5/*"

# Download 3DGS only
hf download nyuvideolab/danceNet3D --repo-type dataset --local-dir DanceNet3D --include "3dgs/*"

Extract Frames

Prerequisites: Python 3.8+, FFmpeg, zstd

pip install -r requirements.txt

# Extract a single session
python video_to_images.py --input s5 --output extracted/s5

# Extract specific sequences
python video_to_images.py --input s5 --output extracted/s5 --sequences AttitudePromenade Chacha

# Extract specific cameras only
python video_to_images.py --input s5 --output extracted/s5 --cameras 0028 1362

# Extract with color LUT correction applied
python video_to_images.py --input s5 --output extracted/s5 --apply-lut

Extract Point Clouds

Prerequisites: zstd, tar

Per-frame sparse point clouds are available under the pointcloud/ directory. Each .tar.zst archive contains one .ply file per frame, named by frame ID (e.g., 0000001.ply).

# Extract a single sequence
mkdir -p BourreeTurns && zstd -d pointcloud/s5/BourreeTurns.tar.zst -o - | tar xf - -C BourreeTurns

# Or two-step
zstd -d pointcloud/s5/BourreeTurns.tar.zst -o BourreeTurns.tar
tar xf BourreeTurns.tar

Using with COLMAP

The colmap/ directory in each sequence contains pre-computed camera intrinsics and extrinsics in COLMAP format. Camera parameters correspond to the undistorted, rotated (portrait orientation) images.

Sequences

Session 4 (s4) — 10 sequences, 29 cameras

Sequence	Frames	Cameras	Status
3PointStep	920	29	Available
BartSimpson	471	29	Available
BizMarkie	703	29	Available
HouseFootwork	937	29	Available
HouseFootworkAdvanced	646	29	Available
RoboCop	920	29	Available
RunningMan	687	29	Available
TheRooftop	983	29	Available
ToeTaps	572	29	Available
WuTang	884	29	Available

Session 5 (s5) — 23 sequences, 29 cameras

Sequence	Frames	Cameras	Status
AttitudePromenade	814	29	Available
BasicSuzieQ	914	29	Available
BigKicks	750	29	Available
BourreeTurns	607	29	Available
BourreeTurns2	688	29	Available
Chacha	942	29	Available
ComboSeated	903	29	Available
DoubleSpiral	769	29	Available
Flair	752	29	Available
Jumping	552	29	Available
Pirouettes	981	29	Available
Portdebras	765	29	Available
PortdebrasSeated	906	29	Unavailable
RonDeJambeAtere	834	29	Available
RonDeJambeAtere2	726	29	Available
RonDeJambeInAir	614	29	Available
SalsaTurns	729	29	Available
Shoulders	682	29	Available
ShouldersSeated	697	29	Coming soon
SonBasic	791	29	Available
SonBasicSeated	924	29	Coming soon
Turns	658	29	Available
Twists	930	29	Available

Session 6 (s6) — 14 sequences, 28 cameras

Sequence	Frames	Cameras	Status
BiancaGolden_Breathing	829	28	Available
BiancaGolden_Chimee	610	28	Available
BiancaGolden_CircleTurns	433	28	Available
BiancaGolden_DropTurn	611	28	Available
BiancaGolden_GrandPlies	1061	28	Available
BiancaGolden_Ocho	476	28	Available
BiancaGolden_Portedbras	940	28	Available
BiancaGolden_ReleasetoFloor	641	28	Available
BiancaGolden_RollDown	1,334	28	Available
BiancaGolden_SalsaBasic	450	28	Available
BiancaGolden_StyleArms	588	28	Available
BiancaGolden_Swings	1,025	28	Available
BiancaGolden_SyncopatedGroove	919	28	Available
RobertRubama_RussiaCostume	751	28	Available

Technical Details

• Resolution: 800 x 1280
• Cameras: 28-29 synchronized Intel RealSense D455
• Frame rate: Captured at 30 fps
• Image format: PNG
• Masks: Binary foreground segmentation via SAM3, stored as PNG
• Calibration: COLMAP format
• Color LUTs: Per-camera 3D lookup tables for color correction

Known Limitations

• Some sequences have small frame gaps, 1-2 frames in the middle of the video, due to capture dropouts
• Video encoding at CRF 18 introduces minor compression artifacts
• Color lut for camera 1000 and camera 1362 are generated by hand with Lightroom to get the visually cloest result. All other cameras were calibrated using a Macbeth chart and OpenCV.

Authors

NYU Video Lab

• Shihang Wei
• Mingjian Li
• Ran Gong

NYU Tandon @ The Yard

• Reese Anspaugh
• Moira Zhang

License

This dataset is owned by New York University (NYU) and released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC-4.0) with additional supplementary terms. See the full LICENSE file for details.