RukawaY/gs_scenes

.
├── train.scene_dataset_config.json       # Habitat scene dataset config (train)
├── val.scene_dataset_config.json         # Habitat scene dataset config (val)
│
├── train/                                # [Category 1] 110 training GS scenes (~24 GB)
│   ├── scene01/                          #   self-reconstructed (full assets)
│   │   ├── scene01.gs.ply               #     3DGS render asset
│   │   ├── scene01.mesh.ply             #     collision mesh
│   │   └── scene01.navmesh              #     navigation mesh
│   ├── scene02/ ... scene55/             #   55 self-reconstructed scenes total
│   ├── interior_0007_840137/             #   InteriorGS (only 3DGS and navmesh)
│   │   ├── interior_0007_840137.gs.ply  #     3DGS render asset
│   │   └── interior_0007_840137.navmesh #     navigation mesh
│   └── interior_0022_840117/ ... ×55     #   55 InteriorGS scenes total
│
├── val/                                  # [Category 1] 19 evaluation GS scenes (~3.3 GB)
│   ├── scene56/ ... scene65/             #   10 self-reconstructed val scenes
│   └── interior_0516_840045/ ... ×9      #   9 InteriorGS val scenes
│
├── avatars/                              # [Category 2] Gaussian avatar assets (~3.1 GB)
│   ├── README.md                         #   how to obtain the SMPL/SMPL-X body models (see below)
│   ├── avatar1/                          #   canonical gaussians of gaussian avatars
│   │   └── canonical_gs.npz
│   ├── avatar2/ ... avatar8/
│   ├── smpl/                             #   SMPL body models — NOT included (license); download yourself
│   │   └── SMPL_{NEUTRAL,MALE,FEMALE}.pkl
│   └── smplx/                            #   SMPL-X body models — NOT included (license); download yourself
│       └── SMPLX_{NEUTRAL,MALE,FEMALE}.{npz,pkl}
│
├── configs/                              # [Category 3, 4, 5 & 6] Hydra YAML configs (~64 KB)
│   ├── ddppo_pointnav_gs_{train,eval}.yaml
│   ├── ddppo_imagenav_gs_{train,eval}.yaml
│   ├── ddppo_objectnav_gs_{train,eval}.yaml
│   ├── ddppo_dynamic_track_gs_{train,eval}.yaml            #   dynamic nav: human tracking
│   ├── ddppo_dynamic_avoid_gs_{train,eval}.yaml            #   dynamic nav: PointNav + avoidance
│   ├── ddppo_dynamic_avoid_imagenav_gs_{train,eval}.yaml   #   dynamic nav: ImageNav + avoidance
│   ├── ddppo_dynamic_avoid_objectnav_gs_{train,eval}.yaml  #   dynamic nav: ObjectNav + avoidance
│   ├── vln_gs_eval.yaml                  #   StreamVLN eval config (hfov=79, turn=15)
│   └── vln_uninavid_gs_eval.yaml         #   Uni-NaVid eval config (hfov=120, turn=30)
│
├── episodes/                             # [Category 3, 4 & 5] Navigation episodes (~80 MB)
│   ├── pointnav/{train,val}/             #   PointNav: 110,000 train + 1,900 val
│   ├── imagenav/{train,val}/             #   ImageNav: 110,000 train + 1,900 val
│   ├── objectnav/{train,val}/            #   ObjectNav: 110,000 train + 1,900 val
│   └── vln/{train,val}/                  #   VLN: 22,000 train + 950 val
│
├── dynamic_nav/                          # [Category 6] Dynamic navigation data (~25 MB)
│   ├── dynamic_nav.scene_dataset_config.json  # Habitat scene dataset config (10 dynamic scenes)
│   ├── scenes/                           #   scene_instance.json per scene: stage + navmesh +
│   │   └── <scene>.scene_instance.json   #     gaussian_avatars wiring (avatar, offset_y, scale)
│   ├── stages/                           #   GS stage templates
│   │   └── <scene>.stage_config.json
│   ├── trajectories/                     #   GAMMA-generated avatar walks (joint_mats + proxy_capsules)
│   │   └── <scene>.driver.pkl            #     one walking avatar per scene, scene01–scene10
│   ├── episodes/{train,val}/             #   PointNav format: 1,000 train + 100 val; agent spawns
│   │                                     #     near the avatar (shared by avoid/imagenav/tracking)
│   └── episodes_objectnav/{train,val}/   #   ObjectNav format: 1,000 train + 100 val
│
└── trajectory_data/                      # [Category 4 & 5] VLN trajectory data
    ├── vln/                              #   StreamVLN trajectories (~40 GB)
    │   ├── annotations.json              #     action sequences + instructions (train)
    │   ├── annotations_val.json          #     action sequences + instructions (val)
    │   └── images/                       #     per-scene tar archives (extract before use)
    │       ├── scene01.tar               #       scene01 trajectories
    │       ├── interior_0007_840137.tar  #       interior_0007 trajectories
    │       └── ...                       #       129 per-scene archives, 22,950 trajectories total
    └── uninavid/                         #   Uni-NaVid trajectories (~25 GB)
        ├── nav_gs_train.json             #     conversation-format annotations (train)
        ├── nav_gs_val.json               #     conversation-format annotations (val)
        └── nav_videos/                   #     per-scene tar archives of .mp4 videos
            ├── scene01.tar               #       scene01 videos
            ├── interior_0007_840137.tar  #       interior_0007 videos
            └── ...                       #       129 per-scene archives, 22,950 videos total

from huggingface_hub import snapshot_download

REPO = "RukawaY/gs_scenes"
LOCAL = "data/scene_datasets/gs_scenes"

# ── Download only GS scenes ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["train/**", "val/**", "*.scene_dataset_config.json"])

# ── Download GS scenes + avatars ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["train/**", "val/**", "*.scene_dataset_config.json", "avatars/**"])

# ── Download everything for Habitat-Lab navigation tasks ──
snapshot_download(REPO, local_dir=LOCAL,
    ignore_patterns=["trajectory_data/**", "avatars/**", "episodes/vln/**"])

# ── Download everything for StreamVLN ──
snapshot_download(REPO, local_dir=LOCAL,
    ignore_patterns=["avatars/**", "episodes/pointnav/**", "episodes/imagenav/**",
                     "episodes/objectnav/**", "trajectory_data/uninavid/**"])

# ── Download everything for Uni-NaVid ──
snapshot_download(REPO, local_dir=LOCAL,
    ignore_patterns=["avatars/**", "episodes/pointnav/**", "episodes/imagenav/**",
                     "episodes/objectnav/**", "trajectory_data/vln/**"])

# ── Download everything for dynamic navigation ──
# needs the 10 scenes (scene01–scene10) + avatars + dynamic_nav data + configs
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["train/scene0*/**", "train/scene10/**", "*.scene_dataset_config.json",
                    "avatars/**", "dynamic_nav/**", "configs/**"])

# ── Download a few specific scenes' trajectories (StreamVLN) ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["trajectory_data/vln/annotations*.json",
                    "trajectory_data/vln/images/scene01.tar",
                    "trajectory_data/vln/images/interior_0007_840137.tar"])

# ── Download a few specific scenes' trajectories (Uni-NaVid) ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["trajectory_data/uninavid/nav_gs_*.json",
                    "trajectory_data/uninavid/nav_videos/scene01.tar",
                    "trajectory_data/uninavid/nav_videos/interior_0007_840137.tar"])

# ── Download everything (~95 GB) ──
snapshot_download(REPO, local_dir=LOCAL)

# StreamVLN trajectories
cd data/scene_datasets/gs_scenes/trajectory_data/vln/images
for f in *.tar; do tar xf "$f" && rm "$f"; done

# Uni-NaVid trajectories
cd data/scene_datasets/gs_scenes/trajectory_data/uninavid/nav_videos
for f in *.tar; do tar xf "$f" && rm "$f"; done

@inproceedings{xia2026habitat,
  title={Habitat-gs: A high-fidelity navigation simulator with dynamic gaussian splatting},
  author={Xia, Ziyuan and Xu, Jingyi and Cui, Chong and Yu, Yuanhong and Zhang, Jiazhao and Yan, Qingsong and Ni, Tao and Chen, Junbo and Zhou, Xiaowei and Bao, Hujun and others},
  booktitle={European Conference on Computer Vision},
  pages={306--323},
  year={2026},
  organization={Springer}
}