  Human head detection, keypoint estimation, and 3D head model fitting are
important tasks with many applications. However, traditional real-world
datasets often suffer from bias, privacy, and ethical concerns, and they have
been recorded in laboratory environments, which makes it difficult for trained
models to generalize. Here, we introduce VGGHeads -- a large scale synthetic
dataset generated with diffusion models for human head detection and 3D mesh
estimation. Our dataset comprises over 1 million high-resolution images, each
annotated with detailed 3D head meshes, facial landmarks, and bounding boxes.
Using this dataset we introduce a new model architecture capable of
simultaneous heads detection and head meshes reconstruction from a single image
in a single step. Through extensive experimental evaluations, we demonstrate
that models trained on our synthetic data achieve strong performance on real
images. Furthermore, the versatility of our dataset makes it applicable across
a broad spectrum of tasks, offering a general and comprehensive representation
of human heads. Additionally, we provide detailed information about the
synthetic data generation pipeline, enabling it to be re-used for other tasks
and domains.
