Motion-Diffusion-Model

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motion-diffusion-model is an Open Source 3D & Animation tool that provides the official PyTorch implementation of the "Human Motion Diffusion Model" paper. It offers fast sampling for human motion generation and editing.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is motion-diffusion-model?

motion-diffusion-model is an open-source PyTorch implementation of the "Human Motion Diffusion Model" paper, designed for generating and editing human motion sequences. The tool boasts significant speed improvements, now running 40X faster with a 50-diffusion-step model and optimized CLIP calling. It supports various tasks including text-to-motion, action-to-motion, and unconstrained motion synthesis. Users can generate motions from text prompts or actions, render SMPL meshes, and perform motion editing such as in-between and upper-body modifications. The project also integrates DiP for ultra-fast text-to-motion and offers features like DistilBERT text encoder support and dataset caching for faster loading.

Best used for

Ideal for researchers and developers who need to implement and experiment with human motion diffusion models, generate realistic character animations from text or actions, and perform advanced motion editing. Especially valuable for academic projects and developing new animation techniques.

Common actions

generate human motion

edit motion sequences

synthesize character animation

implement diffusion models

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Capabilities

Key features

Text-to-motion generation
Action-to-motion synthesis
Motion editing
SMPL mesh rendering
Fast diffusion steps
DistilBERT text encoder
Dataset caching

Target Audience

professordeveloperresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running motion-diffusion-model?

The tool requires Python 3.7, conda3 or miniconda3, and a CUDA-capable GPU. It has been tested on Ubuntu 18.04.5 LTS. Additional dependencies like ffmpeg and specific Python packages are also necessary for full functionality.

How has the performance of motion-diffusion-model improved recently?

The model is now 40X faster due to the release of a 50-diffusion-step model (instead of 1000 steps), which runs 20X faster with comparable results. Additionally, calling CLIP just once and caching the result provides a 2X speed improvement for all models.

Can I train my own motion diffusion models using this implementation?

Yes, the repository includes scripts for training your own MDM models for text-to-motion, action-to-motion, and unconstrained tasks. You can specify datasets like HumanML3D or KIT and configure various training parameters.

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Research & Education › Academic Research Coding & Development › Open Source & Models

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