README.md

---
pretty_name: "Balanced Dog Pose Estimation Dataset (B-DoPED)"
license: other
license_name: research-only
license_link: LICENSE
task_categories:
  - image-to-3d
  - keypoint-detection
size_categories:
  - 10K<n<100K
tags:
  - 3d
  - animals
  - dogs
  - smal
  - synthetic
  - multi-view
  - pose-estimation
---

# Balanced Dog Pose Estimation Dataset (B-DoPED)

> Companion dataset to the ECCV 2026 paper *"Every Dog Has Its Day, Probably"* (see *Citation*).

**B-DoPED** is a synthetic, multi-view dataset of articulated 3D **dogs** built on the **D-SMAL**
model — the dog-specialized variant of [SMAL](https://smal.is.tue.mpg.de/) introduced by
[BITE](https://bite.is.tue.mpg.de/) (model type `39dogs_norm`). It ships three reusable **parameter libraries**
(pose / shape / texture), a **standalone rendering script**, and a large set of pre-rendered
**multi-view outputs**.

## Contents

```
library/
  poses/poses.npz        # pose_6d (35000, 34, 6) float32  — 35k articulated poses (6D joint rotations)
  shapes/shapes.npz      # beta (500,30), betas_limbs (500,9), pose_6d (500,34,6), logscale_part_list (9,)
  textures/              # 1000 flat PNGs (2048²) + one shared UV unwrap
    texture_00000.png … texture_00999.png
    uv_atlas.pth         # shared UV unwrap {vmapping (5187,), faces (7774,3), uvs (5187,2)}
    material.mtl
scripts/                 # standalone renderer + setup
  setup.sh  render_smal_multiview.py  render_utils.py  smal_utils.py  shard_utils.py  load_examples.py
docs/SMAL_SETUP.md
outputs/                 # pre-rendered multi-view results, packed as tar shards
  shard_00000.tar … shard_00019.tar   # 20 shards × 1750 poses; in-tar: pose_NNNNNN/{rgb,seg,npz}/VV.{png,npz}
  shards_index.csv       # per-shard pose range, file/byte counts, sha256
environment.yml  requirements.txt  LICENSE  .gitattributes
```

**Conventions.** A *pose* is `pose_6d (34, 6)` — 6D rotations for 34 joints. Row `k` of `poses.npz`
corresponds to the directory `pose_{k:06d}/` in the render shards. Each pose was rendered from **60
camera views** (4 azimuths × 5 elevations × 3 rolls) at 256×256. For each view a random shape and
texture were sampled; the shape-specific *ear* pose (shape `pose_6d`, joints 32/33) is blended into
the motion pose.

### Per-view NPZ schema (`outputs/.../npz/VV.npz`)

| key | shape | meaning |
|---|---|---|
| `camera/scale`, `camera/tx`, `camera/ty` | scalar | weak-perspective camera |
| `smal/beta` | (1, 30) | D-SMAL shape |
| `smal/betas_limbs` | (1, 9) | limb log-scales (`logscale_part_list`) |
| `smal/pose_6d` | (1, 34, 6) | joint pose (6D), ear-blended |
| `smal/orient_6d` | (1, 6) | camera/global orientation |
| `smal/trans` | (1, 3) | translation |
| `smal/vert_off_compact` | (1, 5901) | per-vertex offsets (compact) |
| `smal/keyp_3d_all` | (1, 47, 3) | 3D keypoints |
| `smal/keyp_2d_all` | (1, 47, 2) | 2D keypoints, normalized to [-1, 1] |
| `smal/keyp_conf` | str | keypoint configuration (`all`) |
| `smal/logscale_part_list` | (9,) | limb part names |
| `smal/smal_model_type` | str | `39dogs_norm` |

## Quickstart

```bash
# 1. (optional) only the library + scripts, not the 92 GB of renders:
hf download 1Konny/B-DoPED --repo-type dataset --local-dir ./pups --exclude "outputs/*"

# 2. environment + SMAL/BITE dependency
conda env create -f environment.yml && conda activate eccv26_bdoped
bash scripts/setup.sh                 # fetches BITE code + D-SMAL weights (see docs/SMAL_SETUP.md)

# 3. peek at the data
python scripts/load_examples.py
```

### Use the libraries directly

```python
import numpy as np
poses  = np.load("library/poses/poses.npz")["pose_6d"]          # (35000, 34, 6)
shapes = np.load("library/shapes/shapes.npz", allow_pickle=True)
betas, limbs = shapes["beta"], shapes["betas_limbs"]            # (500,30), (500,9)
```

### Read the rendered outputs (stream a shard, no unpack)

```python
import io, tarfile, numpy as np
with tarfile.open("outputs/shard_00000.tar") as tar:
    npz = tar.getmember("pose_000000/npz/00.npz")
    d = np.load(io.BytesIO(tar.extractfile(npz).read()), allow_pickle=True)
    print(sorted(d.files))
```

### Render your own

```bash
python scripts/render_smal_multiview.py \
  --pose_npz library/poses/poses.npz \
  --shape_npz library/shapes/shapes.npz \
  --texture_dir library/textures \
  --output_root ./my_renders --bite_root ./bite_gradio-hf \
  --device cuda --resolution 256 --pose_indices_file <(seq 0 9)
# tail_drop_prob 0.1 reproduces the released generation; --shard packs outputs into tar shards.
```

## Partial / selective download

It is a single repository, but the renders are optional:

```bash
hf download 1Konny/B-DoPED --repo-type dataset --exclude "outputs/*"          # library + scripts only
# or in Python:
from huggingface_hub import snapshot_download
snapshot_download("1Konny/B-DoPED", repo_type="dataset", ignore_patterns=["outputs/*"])
# git alternative (skip large LFS blobs, then fetch what you want):
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/1Konny/B-DoPED
```

## License

Released for **non-commercial academic research only** under the
[Research-Only Terms of Use](LICENSE). The authors **claim no ownership** of the upstream
models/assets the dataset derives from and grant no rights beyond those allowed upstream — **you
are responsible for complying with all applicable upstream licenses** (e.g. the D-SMAL / BITE
model; its **weights are not redistributed** here and must be obtained separately, see
[docs/SMAL_SETUP.md](docs/SMAL_SETUP.md)). The Dataset is provided **as is**, without warranty.

## Citation

This dataset accompanies the following paper. If you use it, please cite:

```bibtex
@inproceedings{choi2026everydog,
  title     = {Every Dog Has Its Day, Probably: A Balanced Synthetic Benchmark and
               Probabilistic Modeling for 3D Dog Pose Estimation},
  author    = {Choi, Joo Young and Lee, Wonkwang and Seon, Ju-hyeong and Kim, Gunhee},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026},
}
```