Head Avatars with Dynamic Explicit Hair

We present DynHair, a novel method for tracking and mod-eling dynamic hair for human head avatars. From video input, we recon-struct a dynamic head avatar with an explicit strand-based hair represen-tation using structured 3D Gaussian Splatting. In contrast to the faceregion of human head avatars, which can be modeled with 3D Gaus-sians that are attached or generated with respect to some expressive3D head model, hair is particularly challenging as it exhibits dynamicmotion effects. Therefore, we present a novel method that models thedynamic deformations of the hair strands using a temporal network thatis conditioned on angular velocity and acceleration of the head, as wellas relative gravity. Specifically, an LSTM encodes the motion historyand modulates per-point strand features via FiLM conditioning whichis then used by an MLP to produce physically plausible displacementsto the canonical hairstyle. We jointly optimize this motion and appear-ance representation of the hair, with a 3DGS-based representation ofthe face-region, via differentiable Gaussian splatting with photometric,geometric, and physics-based supervision. As a result, we obtain hair⋆ Equal contribution. † Equal supervision.tracking of the training video data and an animatable head avatar withcontrollable hair dynamics. In our experiments, we demonstrate state-of-the-art performance in terms of hair dynamics, temporal consistency,and generalization across subjects. Our data and code are available athttps://dynhair.is.tue.mpg.de/.