smplx
PyTorch module for loading the SMPLX body model
Decision gist · record as of 2026-08-14
Yes, but with strong caveats. Install if you need SMPL-X for academic research and can work around its abandonment. The package itself installs cleanly with low friction, has no known vulnerabilities, and sees regular downloads. However, it has received no updates since 2021-05-26, so expect no support for newer PyTorch or Python versions, no bug fixes, and no maintenance. The non-commercial license also restricts use to research only. Suitable only for researchers who can maintain their own fork if needed.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Model files (.pkl or .npz) must be downloaded separately from https://smpl-x.is.tuebingen.mpg.de after registration; they are not included in the package.
- Low friction installation via pip.
- The package depends on numpy and torch, both widely available.
License · maintenance · safety
(unclear) — Licensed for non-commercial scientific research only. Commercial use, redistribution, or any use outside academic research is prohibited. You must read and agree to the license terms before downloading or using the model files.
last release 2021-05-26 (1906 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 131,352 downloads/mo, #11,594 on PyPI
Alternatives
Verify before relying
pip install smplx[all]
import torch
from smplx import SMPLX
model = SMPLX(model_path='path/to/model', gender='neutral')
body_pose = torch.zeros(1, 63)
output = model(betas=torch.zeros(1, 10), body_pose=body_pose)- Whether the package works with PyTorch versions released after 2021 without modification.
- Current compatibility with Python 3.10+ given the project's abandonment status.
- Whether the model files remain available for download from the official website.
- Exact number of joints and their configuration across different model variants.
What it is and what it does
SMPL-X is a PyTorch module that loads and evaluates a parametric 3D human body model jointly trained for the body, face, and hands. It represents human shape and pose using learned blend shapes and linear blend skinning, with 10,475 vertices and 54 joints including the neck, jaw, eyeballs, and fingers. The model is defined by pose parameters (θ), shape parameters (β), and facial expression parameters (ψ).
The package provides loaders for multiple related models: SMPL-X (full body with face and hands), SMPL+H (body with detailed hands), SMPL (body only), and MANO (hands only). It requires manual download of model files from the project website and is designed for research applications in 3D human reconstruction, pose estimation, and body animation. The package depends on numpy and torch.
Use it for
- Reconstruct 3D human body, face, and hand geometry from pose and shape parameters in computer vision research.
- Generate synthetic 3D human data for training pose estimation or body shape prediction models.
- Visualize and manipulate human pose and facial expressions in 3D graphics or animation pipelines.
- Transfer pose parameters between different body models (SMPL, SMPL+H, SMPL-X) for cross-model compatibility.
- Extract vertex correspondences between SMPL-X and related models for hand and face analysis.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, but with strong caveats.
Install if you need SMPL-X for academic research and can work around its abandonment. The package itself installs cleanly with low friction, has no known vulnerabilities, and sees regular downloads. However, it has received no updates since 2021-05-26, so expect no support for newer PyTorch or Python versions, no bug fixes, and no maintenance. The non-commercial license also restricts use to research only. Suitable only for researchers who can maintain their own fork if needed.
Install
smplx on PyPI
Before you install
Low friction installation via pip. The package depends on numpy and torch, both widely available. However, the project has been abandoned since its last release on 2021-05-26, so expect no maintenance, bug fixes, or support for newer PyTorch or Python versions.
Model files (.pkl or .npz) must be downloaded separately from https://smpl-x.is.tuebingen.mpg.de after registration; they are not included in the package.
License in practice
Licensed for non-commercial scientific research only. Commercial use, redistribution, or any use outside academic research is prohibited. You must read and agree to the license terms before downloading or using the model files.
Quickstart
pip install smplx[all]
import torch
from smplx import SMPLX
model = SMPLX(model_path='path/to/model', gender='neutral')
body_pose = torch.zeros(1, 63)
output = model(betas=torch.zeros(1, 10), body_pose=body_pose)Verify before relying
- Whether the package works with PyTorch versions released after 2021 without modification.
- Current compatibility with Python 3.10+ given the project's abandonment status.
- Whether the model files remain available for download from the official website.
- Exact number of joints and their configuration across different model variants.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.6.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpytorch |
| Maintenance | Abandoned 1,906 days since the last release |
| First released | |
| Downloads | 131,352 / month, #11,594 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: smplx-0.1.28-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “3D body model human pose”
- smplxLoads and manipulates SMPL-X, a unified 3D body model with 10,475…
- norfairNorfair adds real-time multi-object tracking to any detector by…
- python-fclPython bindings for the Flexible Collision Library (FCL) that perform…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also dex-retargeting · face-alignment · face_recognition_models · facexlib · pipablepytorch3d · mtcnn · deepface · facenet-pytorch · retina-face