shubham-goel/4D-Humans

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1671 stars · 176 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

4DHumans is a Python research codebase implementing HMR 2.0, a transformer-based model for 3D human mesh recovery from single images, plus a PHALP-based system for reconstructing and tracking humans in 3D from monocular video. It is the official code for the ICCV 2023 paper 'Humans in 4D: Reconstructing and Tracking Humans with Transformers'.

Use cases

• reconstruct 3d human meshes from photos
• track multiple people across video frames in 3d
• estimate human pose and shape from a single image
• extract smpl meshes from video for animation or analysis
• run human action recognition features from pose
• process in-the-wild videos with occlusions and unusual poses

When to choose

• you need state-of-the-art single-image 3D human mesh recovery
• you want to track people's identities through occlusion in monocular video
• you need SMPL-format meshes as output for downstream research
• you want a research baseline for pose-based action recognition

When to avoid

• you need real-time performance on edge devices
• you require a production-ready supported product rather than research code
• you cannot register for and obtain the SMPL model files
• you need multi-camera or depth-sensor input rather than monocular RGB

Facets

library · maturity active

machine-learning deep-learning computer-vision image-processing computer-vision machine-learning deep-learning artificial-intelligence python 3d-reconstruction human-mesh-recovery human-pose-estimation transformers hmr2 human-tracking smpl monocular-video research-code iccv-2023 linux macos gpu

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markdown · JSON · MCP: product_card(name="shubham-goel/4D-Humans")