  By equipping the most recent 3D Gaussian Splatting representation with head
3D morphable models (3DMM), existing methods manage to create head avatars with
high fidelity. However, most existing methods only reconstruct a head without
the body, substantially limiting their application scenarios. We found that
naively applying Gaussians to model the clothed chest and shoulders tends to
result in blurry reconstruction and noisy floaters under novel poses. This is
because of the fundamental limitation of Gaussians and point clouds -- each
Gaussian or point can only have a single directional radiance without spatial
variance, therefore an unnecessarily large number of them is required to
represent complicated spatially varying texture, even for simple geometry. In
contrast, we propose to model the body part with a neural texture that consists
of coarse and pose-dependent fine colors. To properly render the body texture
for each view and pose without accurate geometry nor UV mapping, we optimize
another sparse set of Gaussians as anchors that constrain the neural warping
field that maps image plane coordinates to the texture space. We demonstrate
that Gaussian Head & Shoulders can fit the high-frequency details on the
clothed upper body with high fidelity and potentially improve the accuracy and
fidelity of the head region. We evaluate our method with casual phone-captured
and internet videos and show our method archives superior reconstruction
quality and robustness in both self and cross reenactment tasks. To fully
utilize the efficient rendering speed of Gaussian splatting, we additionally
propose an accelerated inference method of our trained model without
Multi-Layer Perceptron (MLP) queries and reach a stable rendering speed of
around 130 FPS for any subjects.
