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1. #REAL ACTION POSE COLLECTION VOL. 2 PDF VK FULL#
2. #REAL ACTION POSE COLLECTION VOL. 2 PDF VK PORTABLE#

Having the face accurately cropped drastically reduces the need for common data augmentations like affine transformations consisting of rotations, translation and scale changes.

#REAL ACTION POSE COLLECTION VOL. 2 PDF VK FULL#

Our ML pipeline consists of two real-time deep neural network models that work together: A detector that operates on the full image and computes face locations and a 3D face landmark model that operates on those locations and predicts the approximate surface geometry via regression. AR effects utilizing facial surface geometry. The analysis runs on CPU and has a minimal speed/memory footprint on top of the ML model inference.įig 1.

#REAL ACTION POSE COLLECTION VOL. 2 PDF VK PORTABLE#

Under the hood, a lightweight statistical analysis method called Procrustes Analysis is employed to drive a robust, performant and portable logic. The face geometry data consists of common 3D geometry primitives, including a face pose transformation matrix and a triangular face mesh. It establishes a metric 3D space and uses the face landmark screen positions to estimate face geometry within that space. Utilizing lightweight model architectures together with GPU acceleration throughout the pipeline, the solution delivers real-time performance critical for live experiences.Īdditionally, the solution is bundled with the Face Geometry module that bridges the gap between the face landmark estimation and useful real-time augmented reality (AR) applications. It employs machine learning (ML) to infer the 3D surface geometry, requiring only a single camera input without the need for a dedicated depth sensor. MediaPipe Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. This site uses Just the Docs, a documentation theme for Jekyll.