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We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robustmodel for 3D Dense Head Alignment in the wild. It contains annotations of over3.5K landmarks that accurately represent 3D head shape compared to theground-truth scans. The data-driven model, DAD-3DNet, trained on our dataset,learns shape, expression, and pose parameters, and performs 3D reconstructionof a FLAME mesh. The model also incorporates a landmark prediction branch totake advantage of rich supervision and co-training of multiple related tasks.Experimentally, DAD-3DNet outperforms or is comparable to the state-of-the-artmodels in (i) 3D Head Pose Estimation on AFLW2000-3D and BIWI, (ii) 3D FaceShape Reconstruction on NoW and Feng, and (iii) 3D Dense Head Alignment and 3DLandmarks Estimation on DAD-3DHeads dataset. Finally, the diversity ofDAD-3DHeads in camera angles, facial expressions, and occlusions enables abenchmark to study in-the-wild generalization and robustness to distributionshifts. The dataset webpage is https://p.farm/research/dad-3dheads.

DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image