RegHead Image Blendshape Dataset

A large-scale dataset of non-humanoid (animal) head identities, each rendered across 10 consistent facial expressions.

This is the official image dataset accompanying [ECCV 2026] RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration. It provides paired multi-expression imagery for a large, diverse population of non-humanoid (animal) head identities — the kind of consistent, expression-aligned supervision needed to learn and evaluate head blendshapes beyond the human face.

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📋 Overview

Each identity is one distinct animal head (e.g. a photorealistic Doberman with round eyes wearing a pirate hat) rendered in the same 10 facial expressions. Because every identity shares the same expression vocabulary, the dataset gives you aligned cross-expression pairs for a fixed identity — directly usable for expression editing, blendshape learning, identity- preserving animation, and registration.

Identities	33,724
Expressions per identity	10 (complete for every identity)
Total images	337,240
Per-sample packaging	1 identity = 1 WebDataset sample (10 images + 1 JSON)
Available resolutions	512×512 and 1024×1024
Image format	JPEG (quality 95)
Container	WebDataset .tar shards
License	Snap Inc. Non-Commercial License (research only)

The 10 expressions

Every identity contains exactly these ten expression images:

Field	Meaning
open_m_open_e	open mouth, open eyes (neutral reference)
halfo_m_o_e	half-open mouth, open eyes
close_m_o_e	closed mouth, open eyes
close_m_halfo_e	closed mouth, 50%-open eyes
close_m_0_25o_e	closed mouth, 25%-open eyes
close_m_0_75o_e	closed mouth, 75%-open eyes
close_m_close_e	closed mouth, closed eyes
close_m_smile	closed-mouth smile
raise_eyebrows	raised eyebrows
frown	frown

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📊 Data distribution

Identities are sampled as combinations of four attributes — an animal, a render style, a facial feature, and 1–2 accessories — yielding broad visual diversity across the population.

Attribute	Pool size	Examples
Animal	~270	Doberman, Polar Bear, Cormorant, Mole, Antelope, Owl, Reindeer, Giant Anteater, …
Render style	4	Photorealistic · Plastic Toy Render · Computer Animated Film-Style 3D · Video Game NPC Style
Feature	~38	round eyes, fluffy cheeks, bushy eyebrows, arched eyebrows, curved horns, …
Accessories	~57	pirate hat, bunny-ear headband, party hat, space helmet, laurel crown, … (1 or 2 per identity)

Render-style distribution (≈ uniform across the four styles):

Style	Identities	Share
Video Game NPC Style	8,600	25.5%
Plastic Toy Render	8,427	25.0%
Photorealistic	8,426	25.0%
Computer Animated Film-Style 3D	8,271	24.5%

Expression completeness: 100% — all 33,724 identities have all 10 expressions (337,240 / 337,240 image fields present). Identities with missing or corrupt frames were excluded prior to release (11 removed from an inspected set of 33,735).

Provenance split: ~59% (19,935) of identities are newly generated for this release via the pipeline below; ~41% (13,789) come from an earlier consistent generation batch using the same expression vocabulary and models.

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🛠️ How the dataset was created

The dataset is produced by a three-stage generative pipeline. A base head is generated, neutralized into a clean reference, then edited into each target expression with a chain of expression-specific image-editing models.

            ┌─────────────────────┐   ┌──────────────────────┐   ┌────────────────────────────┐
 prompt ──▶ │ Stage 0: text→image │──▶│ Stage 1: neutralize  │──▶│ Stage 2: expression edits  │
            │ OpenAI gpt-image-1  │   │ OpenAI (→ open_m_    │   │ Qwen-Image + LoRA adapters │
            │ (sample identity)   │   │  open_e reference)   │   │ (chained, 10 expressions)  │
            └─────────────────────┘   └──────────────────────┘   └────────────────────────────┘

1. Stage 0 — Text-to-image (identity creation). A prompt is assembled from a seeded random combination of animal × style × feature × accessories and rendered with OpenAI's image model to create the base identity.

2. Stage 1 — Neutralization. The base image is converted into a clean, floating-head neutral reference on a white background: the open_m_open_e (open-mouth / open-eyes) frame.

3. Stage 2 — Expression editing. Expression-specific Qwen-Image LoRA adapters edit the reference into the remaining expressions. Edits are chained for consistency:

   • open_m_open_e → (close-mouth adapter) → halfo_m_o_e, close_m_o_e
   • close_m_o_e → (close-eye adapter) → close_m_close_e, close_m_halfo_e, close_m_0_25o_e, close_m_0_75o_e
   • close_m_o_e → (frown/smile/eyebrow adapter) → close_m_smile, raise_eyebrows, frown

   The closed-mouth / open-eyes frame (close_m_o_e) is used as the editing base for the seven downstream expressions so that the mouth stays correctly closed throughout.

Generation is seeded for reproducibility, so the identity set is deterministic and a smaller sample is a strict prefix of a larger one.

Image origin & third-party tools. All images are model-generated (no captured/real photos). Stages 0–1 use the OpenAI GPT image model; Stage 2 uses the Qwen-Image editing model (Apache-2.0) with custom LoRA adapters. See License for terms.

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🔬 Two releases: 512 vs 1024

The dataset ships in two resolution variants with identical content, identity sets, expressions, metadata, and per-sample layout — they differ only in image resolution and total size. Pick the one that fits your compute and resolution needs.

	512 release	1024 release
Release tag	reghead-webdataset-v1-512	reghead-webdataset-v1-1024
Resolution	512 × 512	1024 × 1024
Image format	JPEG q95	JPEG q95
Identities	33,724	33,724
Images	337,240	337,240
Shards (.tar)	11	34
Total size	18.96 GiB (19,412 MiB)	58.79 GiB (60,200 MiB)
Max shard size	< 1900 MiB (GitHub-Release safe)	< 1900 MiB (GitHub-Release safe)
Best for	fast prototyping, lightweight training, limited bandwidth/disk	high-resolution training, detail-sensitive editing & evaluation

Both variants encode the same source frames; the 512 release is a downscaled copy. Train/develop on 512, then scale to 1024 without changing any data-loading code — only the shard URLs change.

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📦 Asset layout

Each release consists of:

reghead-v1-<res>-000000.tar      # WebDataset image shards (the bulk of the data)
reghead-v1-<res>-000001.tar
...
metadata.tar.gz                  # release_manifest.json + webdataset_samples.jsonl
checksums.tar.gz                 # SHA256SUMS for every shard

Inside each shard, every identity is one sample — a group of files sharing a common key:

00000000_id000_Doberman_Dog_..._64896c31.open_m_open_e.jpg
00000000_id000_Doberman_Dog_..._64896c31.close_m_o_e.jpg
...                              # 10 .jpg files, one per expression
00000000_id000_Doberman_Dog_..._64896c31.json     # per-sample metadata

Per-sample JSON schema:

{
  "identity": "id000_Doberman_Dog_Photorealistic_round_eyes_a_wreath_a_pirate_hat",
  "identity_dir": "id000_Doberman_Dog_Photorealistic_round_eyes_a_wreath_a_pirate_hat",
  "sample_key": "00000000_id000_Doberman_Dog_..._64896c31",
  "num_expressions": 10,
  "expressions": [
    {"expression": "close_m_0_25o_e", "field": "close_m_0_25o_e.jpg", "original_file": "close_m_0_25o_e.jpg"},
    ...
  ]
}

metadata/webdataset_samples.jsonl provides a flat index (one row per identity: sample_key → identity → shard → num_expressions), and metadata/release_manifest.json lists every shard with its size and SHA-256.

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🚀 How to use

1. Download

# 512 release (smaller, ~19 GiB)
gh release download reghead-webdataset-v1-512 \
  --repo snap-research/RegHead --dir reghead_download

# or the 1024 release (~59 GiB)
gh release download reghead-webdataset-v1-1024 \
  --repo snap-research/RegHead --dir reghead_download

2. Verify checksums (optional but recommended)

cd reghead_download
tar -xzf checksums.tar.gz                    # extracts checksums/SHA256SUMS
mkdir -p data && mv reghead-v1-*.tar data/   # SHA256SUMS paths are data/-relative
sha256sum -c checksums/SHA256SUMS

3. Extract metadata

tar -xzf metadata.tar.gz                     # → metadata/{release_manifest.json,webdataset_samples.jsonl}

4. Load with WebDataset

pip install webdataset pillow

import glob, json
import webdataset as wds

urls = sorted(glob.glob("reghead_download/data/*.tar"))

EXPRESSIONS = [
    "open_m_open_e", "halfo_m_o_e", "close_m_o_e", "close_m_halfo_e",
    "close_m_0_25o_e", "close_m_0_75o_e", "close_m_close_e",
    "close_m_smile", "raise_eyebrows", "frown",
]

def parse_identity_sample(sample):
    meta = sample["json"]
    if isinstance(meta, (bytes, bytearray)):
        meta = json.loads(meta.decode("utf-8"))
    images = {expr: sample[f"{expr}.jpg"] for expr in EXPRESSIONS
              if f"{expr}.jpg" in sample}
    return {"identity": meta["identity"], "images": images, "metadata": meta}

dataset = wds.WebDataset(urls).decode("pil").map(parse_identity_sample)

for item in dataset:
    print(item["identity"], list(item["images"].keys()))
    # item["images"]["frown"] is a PIL.Image
    break

Note: image fields use the .jpg extension. Decode with .decode("pil") to get PIL.Image objects (or .decode("torchrgb") for tensors).

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📁 What this dataset does not include

Per the license, this release contains only the 2D animal-head images and their accompanying metadata. It does not include any 3D models, meshes, textures, blendshape rigs, model weights, checkpoints, the registration model, or training code.

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📜 License

This dataset is released under the Snap Inc. Non-Commercial License — for non-commercial, research purposes only. See the LICENSE file for the full text.

Third-party tools used to generate the imagery are governed by their own terms:

• OpenAI GPT image model — used to generate neutral-expression animal-head images.
• Qwen-Image editing model — used to generate the expression edits; Qwen-Image is licensed under the Apache License 2.0.

No third-party software, model weights, checkpoints, or code is licensed under this license.

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📝 Citation

If you use this dataset, please cite RegHead:

@inproceedings{reghead2026,
  title     = {RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026}
}