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.

本研究解决了传统真实世界数据集在偏见、隐私和伦理等问题带来的挑战，提出了VGGHeads，一个通过扩散模型生成的大规模合成数据集。该数据集包含超过100万张高分辨率图像，具有详尽的3D人头网格和人脸关键点标注，研究表明，基于此数据集训练的模型能在真实图像上表现出色，具有广泛的应用潜力。

VGGHeads：一个大规模合成数据集用于3D人头模型