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.

翻译：通过将最新的3D高斯泼溅表示与三维头部可变形模型（3DMM）相结合，现有方法已能创建高保真度的头部虚拟人。然而，多数方法仅重建头部而不包含身体，这极大限制了其应用场景。我们发现，将高斯模型直接应用于带衣物的胸肩部位时，新姿态下往往会出现重建模糊和噪声漂浮物。这是由于高斯模型和点云的根本局限——每个高斯或点只能具有无空间变化的单向辐射度，因此即使对于简单几何结构，也需要大量此类单元来表示复杂的空间变化纹理。相比之下，我们提出采用由粗粒度颜色和姿态相关细粒度颜色组成的神经纹理来建模身体部位。为了在没有精确几何或UV映射的情况下正确渲染每个视角和姿态下的身体纹理，我们优化了一组稀疏高斯作为锚点，以约束将图像平面坐标映射至纹理空间的神经扭曲场。实验证明，高斯头肩模型能够高保真地拟合衣物上半身的细微细节，并可能提升头部区域的精度与保真度。我们使用手机拍摄和互联网视频进行方法评估，结果表明本方法在自驱动与交叉驱动任务中均具有卓越的重建质量和鲁棒性。为充分释放高斯泼溅的快速渲染潜力，我们还提出无需多层感知器（MLP）查询的加速推理方法，对任意主体实现约130 FPS的稳定渲染速度。