This paper presents GEA, a novel method for creating expressive 3D avatars with high-fidelity reconstructions of body and hands based on 3D Gaussians. The key contributions are twofold. First, we design a two-stage pose estimation method to obtain an accurate SMPL-X pose from input images, providing a correct mapping between the pixels of a training image and the SMPL-X model. It uses an attention-aware network and an optimization scheme to align the normal and silhouette between the estimated SMPL-X body and the real body in the image. Second, we propose an iterative re-initialization strategy to handle unbalanced aggregation and initialization bias faced by Gaussian representation. This strategy iteratively redistributes the avatar's Gaussian points, making it evenly distributed near the human body surface by applying meshing, resampling and re-Gaussian operations. As a result, higher-quality rendering can be achieved. Extensive experimental analyses validate the effectiveness of the proposed model, demonstrating that it achieves state-of-the-art performance in photorealistic novel view synthesis while offering fine-grained control over the human body and hand pose.
本文提出了GEA，一种创新的方法，用于基于3D高斯创建表情丰富的3D化身，具有高保真度的身体和手部重建。主要贡献有两方面。首先，我们设计了一种两阶段姿态估计方法，从输入图像中获得准确的SMPL-X姿态，提供了训练图像的像素与SMPL-X模型之间的正确映射。它使用一个注意力感知网络和一个优化方案，以对齐估计的SMPL-X身体与图像中真实身体的法线和轮廓。其次，我们提出了一种迭代重初始化策略，以处理高斯表示面临的不平衡聚合和初始化偏差。这一策略通过应用网格化、重采样和重新高斯化操作，迭代地重新分配化身的高斯点，使其在人体表面附近均匀分布。结果是，可以实现更高质量的渲染。广泛的实验分析验证了所提模型的有效性，证明了它在真实感新视角合成中达到了最先进的性能，同时提供了对人体和手部姿态的细粒度控制。