# SizheAn/PanoHead

Code Repository for CVPR 2023 Paper "PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360 degree"

Repository: https://github.com/SizheAn/PanoHead
Canonical: https://ross.abutalabs.com/products/panohead
Language: Python
License: CC0-1.0
License Family: permissive
Last push: 2024-02-05T15:29:11+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 84
- inputs: {"age_days": 1177, "days_push": 940, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1956, forks 236 (observed 2026-08-28T04:05:59.080903+00:00)

## What it is
PanoHead is the official PyTorch implementation of a CVPR 2023 paper presenting a 3D-aware GAN that synthesizes geometry-aware, view-consistent full human heads in 360 degrees from in-the-wild images. It also supports reconstructing personalized 3D head avatars from a single input photo.

## Use cases
- generate 3d consistent full-head images from any viewing angle
- create a 3d avatar from a single photo
- train a 3d gan on unstructured in-the-wild face images
- render 3d heads with detailed geometry and hairstyles
- research on 3d-aware generative adversarial networks

## When to choose
- you need 360-degree view-consistent 3D head synthesis or single-image 3D avatar reconstruction
- you have NVIDIA GPUs and want a research-grade 3D GAN codebase to build on

## When to avoid
- you need a production-ready end-user application rather than research code
- you lack a CUDA-capable NVIDIA GPU
- you need real-time synthesis on consumer hardware without GPU setup

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, computer-vision, graphics
- domain: deep-learning, computer-vision, graphics, artificial-intelligence
- platform: python
- tags: 3d-gan, head-synthesis, avatar-generation, cvpr-2023, neural-rendering, research-code, linux, gpu, cuda

## Member repositories
- SizheAn/PanoHead (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:59.080903+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:06:13.194764+00:00, confidence not recorded.
  - readme: https://github.com/SizheAn/PanoHead (fetched 2026-08-28T04:05:59.080903+00:00, sha 6e6e4a2182b9)
- Data as of 2026-08-30T08:39:29.467469+00:00.