metadata
license: mit
library_name: coreml
pipeline_tag: keypoint-detection
tags:
- coreml
- core-ml
- ios
- macos
- apple
- on-device
- face-alignment
- 3dmm
- head-pose
- arxiv:2009.09960
3DDFA V2 — Core ML
3D Face Reconstruction, 2020
Single-image 3D face reconstruction. Predicts 6 DoF pose + expression parameters.
Core ML conversion of cleardusk/3DDFA_V2 for on-device inference on iPhone, iPad and Mac. Converted with coremltools; the packages are stateless, so all sequencing and buffering lives in your Swift code.
| Task | keypoint detection |
| Upstream | cleardusk/3DDFA_V2 |
| Packages | 1 |
| Download size | 6 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~200 MB |
Files
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
3DDFA_V2.mlpackage.zip |
6 MB | all |
0f715dc220c046f5… |
| Total | 6 MB |
compute_units is not a suggestion -- it is the configuration the conversion was verified against. Moving a package to a different compute unit can silently change the numerics (FP16 attention overflow) or crash on the GPU.
Download
hf download mlboydaisuke/coreml-zoo --include "face3d/*" --local-dir ./face3d
unzip './face3d/face3d/*.zip' -d ./face3d
Use in Swift
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .all // as converted — see the table above
// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try 3DDFA_V2(configuration: config)
// ...or compile a downloaded .mlpackage at runtime:
let compiled = try await MLModel.compileModel(at: mlpackageURL)
let model = try MLModel(contentsOf: compiled, configuration: config)
Demo
- Sample app —
sample_apps/Face3DDemo, a standalone SwiftUI project. - Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.
Conversion
- Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling):
docs/coreml_conversion_notes.md - Model index: CoreML-Models
License
The conversion inherits the upstream license: MIT.
Credits
- Upstream authors: cleardusk/3DDFA_V2, 2020
- Core ML conversion: john-rocky (Daisuke Majima)