3d Morphable Face Model Fitting And Manipulation

via “head pose and gaze direction control”

LivePortrait — AI demo on HuggingFace

Unique: Decouples head pose from facial expression through a 3D morphable face model that separates rigid head transformation from non-rigid expression deformation, enabling independent control without expression artifacts during rotation

vs others: More geometrically accurate than 2D warping-based approaches and faster than full 3D face reconstruction because it uses a lightweight parametric face model with learned pose regression rather than iterative optimization

SadTalker — AI demo on HuggingFace

Unique: Uses a parametric 3D morphable face model as an intermediate representation, enabling explicit control over identity, expression, and pose as separate parameters. Fitting is done via differentiable rendering, allowing end-to-end optimization and gradient-based manipulation of facial attributes.

vs others: More interpretable and controllable than implicit 3D representations (NeRF, voxel grids) because parameters directly correspond to semantic facial attributes, enabling fine-grained expression transfer and pose manipulation without retraining.

via “facial landmark detection and tracking”

FacePoke_CLONE-THIS-REPO-TO-USE-IT — AI demo on HuggingFace

Unique: Integrates landmark detection directly into the HuggingFace Spaces inference pipeline, leveraging Gradio's built-in video input handling and model caching to avoid redundant model loads across requests

vs others: More accessible than raw OpenCV/dlib implementations because it abstracts model loading and preprocessing; faster iteration than building custom PyTorch models because it uses pre-trained weights from HuggingFace Model Hub

via “facial landmark detection and alignment with geometric transformation”

Unique: Implements multi-stage landmark detection and TPS-based geometric alignment to handle head rotation and scale differences, ensuring swapped faces are properly positioned rather than naively overlaid — this is a core differentiator from simple image-blending approaches

vs others: More robust geometric alignment than basic bounding-box approaches, but less sophisticated than 3D morphable model-based methods used in research (e.g., Basel Face Model) which require more computational resources