shunsukesaito/PIFu

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1817 stars · 346 forks observed · 2026-08-28

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

PyTorch implementation of PIFu (Pixel-Aligned Implicit Function), a deep learning method that reconstructs high-resolution 3D surfaces and textures of clothed humans from single or multiple images. It is the official research codebase for the ICCV 2019 paper, including test, training, and synthetic training-data generation pipelines.

Use cases

• reconstruct a 3d human model from a single photo
• generate textured 3d meshes of clothed people from images
• digitize clothing wrinkles, hairstyles, and accessories into 3d geometry
• create 3d printable models of a person from a picture
• train a neural network to infer 3d human shape from 2d images
• build ar/vr avatar assets from real-world photos

When to choose

• you need research-grade code for pixel-aligned implicit surface reconstruction of clothed humans
• you want to reproduce or build on the ICCV 2019 PIFu paper results
• you need high-resolution 3D human reconstruction with texture that handles arbitrary topology and unseen regions
• you want to generate your own training data with headless GPU rendering via EGL

When to avoid

• you need a polished end-user application with a GUI rather than research code requiring Python and PyTorch setup
• you need real-time or production-supported 3D human reconstruction
• you want to reconstruct generic objects rather than human subjects
• you require actively maintained software with current framework versions (code targets older PyTorch and CUDA)

Facets

library · maturity maintenance

deep-learning computer-vision image-processing machine-learning graphics data-generation computer-vision deep-learning machine-learning graphics image-processing python cross-platform windows 3d-reconstruction human-digitization implicit-function pytorch single-image-3d mesh-generation texture-reconstruction iccv2019 3d-printing arvr research-code geometry-processing gpu linux

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markdown · JSON · MCP: product_card(name="shunsukesaito/PIFu")