AnimalLift

Official public dataset for the SIGGRAPH Asia 2026 paper
AnimalLift: Reconstructing Animatable 3D Animals from a Single Image via Canonical Shape, Texture, and Fur

AnimalLift is the public dataset released with the SIGGRAPH Asia 2026 paper AnimalLift: Reconstructing Animatable 3D Animals from a Single Image via Canonical Shape, Texture, and Fur. The paper presents a framework for reconstructing animation-ready 3D animals from a single image using a shared canonical representation of shape, texture, and fur.

The dataset contains paired animal images, UV textures, shared-topology 3D meshes, and UV-aligned fur maps across multiple animal categories. These assets were generated to provide canonical supervision for geometry, appearance, and strand-level fur reconstruction.

The repository also includes load_dataset_sample.py, a Blender visualization script that loads one dataset sample, reconstructs hair curves, assigns materials, configures HDR environment lighting, and optionally saves a rendered image or .blend file. We test it on Blender 4.2.

Dataset contents

The dataset is organized by animal category:

AnimalLift/
├── dog/
│   └── dataset/
├── small_cat/
│   └── dataset/
├── big_cat/
│   └── dataset/
├── wolf/
│   └── dataset/
├── fox/
│   └── dataset/
├── bear/
│   └── dataset/
├── meta/
│   ├── hair_map_meta_uvanchor.npz
│   ├── render_elements/
│   │   └── hdr/
│   │       └── *.hdr
│   └── used_version_obj/
├── training_assets/
│   └── UV_Occupancy.png
└── load_dataset_sample.py

Each dataset group generally follows this structure:

<animal>/dataset/<group>/
├── augmented_images/
│   └── *.png
├── textures/
│   └── *.png
├── shapes/
│   └── *.obj
├── hair_maps_single_512_uvlocal/
│   └── *.pt
└── hair_info.json

Some groups may also contain legacy .npz hair maps or optional deformed meshes.

Modalities

Augmented images

augmented_images/ contains image inputs associated with the samples in a group.

Textures

textures/ contains UV texture maps. Texture files are paired with shapes using natural filename ordering.

Shapes

shapes/ contains OBJ meshes with UV coordinates.

Hair maps

hair_maps_single_512_uvlocal/ contains compressed UV-local hair representations.

The preferred .pt format stores:

{
    "hair_q": ...,       # int8 tensor [H, W, S - 1, 3]
    "scale": ...,        # float32 tensor [3]
    "hair_mask": ...,    # bool tensor [H, W]
}

Legacy .npz files may contain:

hair_offsets_local_q
offset_scale
samples

The global metadata file meta/hair_map_meta_uvanchor.npz provides the UV-to-surface mapping required to reconstruct strands:

group_names
guide_group_id_map
uv_face_index_map
uv_bary_map

Download

Clone the dataset repository:

git lfs install
git clone https://huggingface.co/datasets/Chunyi99/AnimalLift
cd AnimalLift

For large downloads, the Hugging Face CLI can also be used:

pip install -U huggingface_hub
hf download Chunyi99/AnimalLift \
  --repo-type dataset \
  --local-dir /path/to/animallift

Blender visualization

The visualization entry point is:

load_dataset_sample.py

Requirements

• Blender with Python scripting support
• NumPy
• PyTorch in Blender's Python environment when loading .pt hair maps
• Blender procedural hair Geometry Nodes assets
• Cycles support for final rendering

The script uses Blender modules such as bpy, bmesh, and mathutils, so it must be executed by Blender rather than by a standard Python interpreter.

Configure the dataset root

Open load_dataset_sample.py and set:

ANIMALLIFT_ROOT = Path("/path/to/animallift")

The directory must contain the animal category folders and the meta/ directory.

Do not commit a machine-specific absolute path.

Run the default sample

blender --python load_dataset_sample.py

When arguments are supplied, place them after --:

blender --python load_dataset_sample.py -- \
  --species dog \
  --group_name groupA_different_breeds_chaiquan \
  --sample_index 0

Load a specific OBJ

blender --python load_dataset_sample.py -- \
  --obj_path /path/to/animallift/dog/dataset/groupA_different_breeds_chaiquan/shapes/aug_01.obj

The script resolves the texture and hair map from the same dataset group.

Save a Blender scene

blender --python load_dataset_sample.py

Command-line arguments

Argument	Description
--obj_path	Optional path to a specific OBJ inside a group shapes/ directory.
--species	Animal category used for automatic sample discovery.
--group_name	Dataset group used for automatic sample discovery.
--sample_index	Natural-sort index of the sample inside the selected group.
--mesh_object_name	Name assigned to the imported Blender mesh object.
--deformed_obj_path	Optional deformed OBJ path.
--shape_key_name	Name used when applying deformation as a shape key.
--shape_key_value	Shape-key influence value.
--save_blend_path	Optional output path for a .blend file.
--render_filename	Optional output render filename.
--use_shape_key	Use the legacy shape-key deformation path.

Paper

This dataset accompanies the SIGGRAPH Asia 2026 paper:

AnimalLift: Reconstructing Animatable 3D Animals from a Single Image via Canonical Shape, Texture, and Fur

The paper introduces:

• a canonical shared-topology representation for animal geometry, texture, and fur;
• a procedural dataset generation pipeline covering diverse species, body shapes, poses, textures, and fur properties;
• a UV-aligned fur map that explicitly encodes strand geometry and supports deformation, animation, and simulation;
• a single-image feed-forward reconstruction framework that jointly predicts canonical shape, texture, and fur.

The current manuscript describes a dataset with shared topology and UV parameterization, including 3,000 dog shapes, 3,000 cat shapes, 500 lion and tiger shapes, and 1,500 shapes each for wolves, foxes, and bears. Textures are generated at 1024 × 1024 resolution, while the canonical fur map is defined on a 512 × 512 UV grid.

Citation

The uploaded manuscript is currently anonymized and contains a placeholder DOI. Until the final bibliographic record is available, cite the project using the temporary entry below:

@article{animallift2026,
  title   = {AnimalLift: Reconstructing Animatable 3D Animals from a Single Image via Canonical Shape, Texture, and Fur},
  author  = {Anonymous Authors},
  year    = {2026},
  note    = {SIGGRAPH Asia 2026}
}

Replace the author list, volume, issue, article number, pages, and DOI with the final publication metadata once released.

Indexed content

• Samples: 11545
• Uploaded data files: 48490
• Data size: 295.51 GiB
• Guidance directory: augmented_images
• Texture directory: textures
• Shape directory: shapes
• Hair directory: hair_maps_single_512_uvlocal
• Recursive indexing: False

Each species is stored under:

<species>/dataset/<original relative path>

Original group names are replaced by deterministic names such as bear000001. Numbering restarts within each species. The upload uses hard links, so no full data copy is created. manifest.json records the original-to-new name mapping.