Face-specialized monocular depth estimation. Trained on a dataset of labelled faces, for use on Apple Coreml, Great for selfie videos, Portrait videos, clothing and a wide variety of human faces

General depth models train on streets, rooms, and furniture, so they flatten a face into a smooth blob. A face holds its information in millimeters of relief, the bridge of the nose against the cheek, the small step from lid to eyeball, and that signal sits far below what a scene-trained model represents. Face relighting, avatar capture, and AR effects need the part those models throw away.

FaceDepth is a Depth Anything V2-Large variant tuned to keep that detail. It resolves the eyelid crease, the nostril rim, the lip contour, and the hairline.

Code and paper: github.com/AristidesAI/FaceDepth · Instagram: @aristides.lab

Measured on 500 held-out faces.

Edge metrics are density-matched at 5% of in-face gradient pixels with a 2-pixel tolerance, so a blurry model cannot win by spreading weak gradients across the whole face. Recall and precision both rise, so the model finds real depth edges and stops inventing ones that are not there.

Every Core ML export correlates at 1.00000 against the PyTorch reference. Benchmarks use 392x518 input on an Apple-silicon laptop with ComputeUnit.ALL, averaged over 20 runs after warmup.

On this hardware int8 buys a 2x size reduction rather than speed, so its advantage is the app bundle. The ranking may differ on iPhone, where the Neural Engine is relatively stronger, and that has not been measured.

Input is a 392x518 portrait RGB image. ImageNet normalization is folded into the graph, so pass raw pixels.

The checkpoint also carries a conf_head. Inference does not need it and the Core ML exports drop it.

The model returns relative inverse depth with an arbitrary scale. Normalize inside the face, not across the frame. A whole-frame min-max collapses the face's range the moment a distant background enters the shot, which reads as a black or washed-out face. This one detail causes most of the "the model looks broken" reports.

video_depth.py converts any video ffmpeg can read, up to 4K, into a depth-map video. Frames stream through an ffmpeg pipe, so a long clip never lands on disk as a frame dump and memory stays flat.

Colormaps: inferno, magma, turbo, viridis, plasma, bone, gray. --range-ema smooths the near/far range across frames and is on by default with no ghosting. --smooth-depth smooths depth itself, which cuts residual jitter but ghosts behind fast motion. Needs ffmpeg on PATH and Depth-Anything-V2 cloned into third_party/DepthAnythingV2.

This model optimizes single-image sharpness, so live video flickers more than a temporally stabilized model would. For offline video, video_depth.py damps that with --range-ema and --smooth-depth.

Detail is capped by the reference the model learned from. The claim is sharp feature relief and boundaries, not sub-millimeter texture. Per-eyelash depth is beyond what any current monocular model resolves.

Training data is centered, well-lit, and limited in pose, occlusion, and demographic diversity relative to in-the-wild use. Performance by demographic group has not been measured. Evaluate before deploying on populations or capture conditions that differ from the training distribution.

Released under CC-BY-NC-4.0, non-commercial research use.

FaceDepth derives from Depth Anything V2-Large, which is CC-BY-NC-4.0. It builds on Apple Depth Pro and CelebAMask-HQ, whose terms restrict use to non-commercial research and education. Honor the upstream terms of all three.

Please also cite the work this builds on: Depth Anything V2 (arXiv:2406.09414), Depth Pro (arXiv:2410.02073), MiDaS (arXiv:1907.01341), DPT (arXiv:2103.13413), DINOv2 (arXiv:2304.07193), and CelebAMask-HQ (arXiv:1907.11922).