ai-agent

MultiPoseNet.pytorch: Fast Multi‑Person Pose Estimation in PyTorch

Accurate human pose detection ready for your AI‑driven fitness, gaming, or AR product—perfect for founders needing reliable pose estimation without building from scratch.

From the repo · “pytorch implementation of MultiPoseNet (ECCV 2018, Muhammed Kocabas et al.)”

195 stars32 forksPythonGuide quality 9/10 · reviewed Oct 10Updated 5/23/2019100% free · open source
What it does

MultiPoseNet.pytorch estimates human poses in images or videos, allowing founders to build AI-powered apps that track and analyze human movement

When to use it
  • •Building a fitness app that tracks exercise form
  • •Developing a smart surveillance system that detects and tracks people
  • •Creating a gaming experience that responds to player movement
Ready-to-paste prompt
python demo.py --input ../images/test_image.jpg --output ../output --format json
Heads up: Ensure you have PyTorch 1.9 or later installed, as earlier versions may not be compatible with MultiPoseNet.pytorch
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/multiposenet-pytorch && curl -fsSL https://workflowstacks.com/api/skills/multiposenet-pytorch/claude-skill -o ~/.claude/skills/multiposenet-pytorch/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How MultiPoseNet.pytorch: Fast Multi‑Person Pose Estimation in PyTorch works
Codeflow
Free to inspect

MultiPoseNet.pytorch: Fast Multi‑Person Pose Estimation in PyTorch is a medium Python project (~4.4k lines across 51 code files). Expect some technical setup — comfortable with a terminal, or ask a developer. Last commit 90 months ago, no license file, has a test suite.

Size
Medium codebase
~4.4k lines · 51 code files · 37 min skim
Setup
Some technical setup
Comfortable with a terminal? 20–40 min. Otherwise ask a dev.
Runs on
Python
No API keys detected
Python 95%C 3%Cuda 2%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    lib/__init__.py
    Inside lib/ — the main logic begins here
  3. 3
    demo/models/README.md
    A worked example — copy this to get going
What's in each folder
lib/Core code — the actual logic22 files
demo/Examples you can copy5 files
datasets/Data files11 files
network/Folder8 files
evaluate/Folder7 files
training/Folder6 files
configs/Configuration1 files
READMEHas testsThin docsNo CIExamples includedNo licenseLast update 90 mo ago
Quick Actions
Details
Creator
LiMeng95
Language
Python
Category
ai-agent
Published
9/23/2018

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