--- 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 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}, } ```