cleardusk/3DDFA

Health v2 · maintenance only

Adoption not part of the score

3677 stars · 642 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch implementation of the TPAMI 2017 paper 'Face Alignment in Full Pose Range: A 3D Total Solution' (3DDFA). It fits a 3D Morphable Model to a single face image to produce 3D face shapes, 68 landmarks, pose estimation, depth images, PNCC/PAF features, and OBJ mesh serialization.

Use cases

• fit a 3D face model to a photo
• detect 68 facial landmarks in large pose images
• estimate head pose from a single image
• generate depth maps of faces
• export 3D face meshes as OBJ files
• compute PNCC and PAF features for face analysis

When to choose

• you need 3D face alignment or dense face reconstruction in Python with PyTorch
• you want landmark detection robust to large pose ranges
• you need face pose estimation, depth, or mesh outputs from a single image

When to avoid

• you need the faster, more accurate successor (use 3DDFA_V2 instead)
• you need a maintained production pipeline - the repo is in maintenance mode with its last release in 2022
• you work outside Python/PyTorch and don't want to use the simple C++ port

Facets

library · maturity maintenance

computer-vision machine-learning deep-learning image-processing computer-vision deep-learning python cross-platform face-alignment 3dmm pytorch landmarks pose-estimation 3d-face-reconstruction 3d-face

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