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mingyuan-zhang/MotionDiffuse

0
View on GitHub↗
976 stars·85 forks·Python·9 viewsmingyuan-zhang.github.io/projects/MotionDiffuse.html↗

MotionDiffuse

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

Features

  • Motion Generation - Generates human motion sequences driven by text prompts using diffusion models.

Star history

Star history chart for mingyuan-zhang/motiondiffuseStar history chart for mingyuan-zhang/motiondiffuse

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Open-source alternatives to MotionDiffuse

Similar open-source projects, ranked by how many features they share with MotionDiffuse.
  • athn-nik/sincA

    athn-nik/sinc

    0View on GitHub↗

    SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation Nikos Athanasiou · Mathis Petrovich · Michael J. Black · Gül Varol ICCV 2023 Official PyTorch implementation of the paper "SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation"

    View on GitHub↗0
  • athn-nik/teachA

    athn-nik/teach

    0View on GitHub↗

    TEACH: Temporal Action Compositions for 3D Humans Nikos Athanasiou · Mathis Petrovich · Michael J. Black · Gül Varol 3DV 2022

    View on GitHub↗0
  • guytevet/motion-diffusion-modelGuyTevet avatar

    GuyTevet/motion-diffusion-model

    4,054View on GitHub↗

    This is a PyTorch deep learning framework and tool for human motion synthesis that generates 3D character animations from text prompts or action descriptions. It functions as a text-to-motion generator that converts natural language and categorical labels into temporally consistent 3D skeletal movement sequences. The system utilizes a transformer-based diffusion model to iteratively denoise motion data. It includes capabilities for action-conditioned generation, monocular-to-3D motion lifting, and motion sequence editing using text constraints. The framework incorporates geometric motion con

    Python
    View on GitHub↗4,054
  • guytevet/motionclipGuyTevet avatar

    GuyTevet/MotionCLIP

    497View on GitHub↗

    Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space".

    Python
    View on GitHub↗497
See all 6 alternatives to MotionDiffuse→

Frequently asked questions

What does mingyuan-zhang/motiondiffuse do?

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

What are the main features of mingyuan-zhang/motiondiffuse?

The main features of mingyuan-zhang/motiondiffuse are: Motion Generation.

What are some open-source alternatives to mingyuan-zhang/motiondiffuse?

Open-source alternatives to mingyuan-zhang/motiondiffuse include: athn-nik/sinc — SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation Nikos Athanasiou · Mathis Petrovich ·… athn-nik/teach — TEACH: Temporal Action Compositions for 3D Humans Nikos Athanasiou · Mathis Petrovich · Michael J. Black · Gül… guytevet/motion-diffusion-model — This is a PyTorch deep learning framework and tool for human motion synthesis that generates 3D character animations… guytevet/motionclip — Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space". mathux/temos — Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions", ECCV… mathux/tmr — Mathis Petrovich · Michael J. Black · Gül Varol.