Recent advancements in radiance field rendering show promising results in 3D scene representation, where Gaussian splatting-based techniques emerge as state-of-the-art due to their quality and efficiency. Gaussian splatting is widely used for various applications, including 3D human representation. However, previous 3D Gaussian splatting methods either use parametric body models as additional information or fail to provide any underlying structure, like human biomechanical features, which are essential for different applications. In this paper, we present a novel approach called HFGaussian that can estimate novel views and human features, such as the 3D skeleton, 3D key points, and dense pose, from sparse input images in real time at 25 FPS. The proposed method leverages generalizable Gaussian splatting technique to represent the human subject and its associated features, enabling efficient and generalizable reconstruction. By incorporating a pose regression network and the feature splatting technique with Gaussian splatting, HFGaussian demonstrates improved capabilities over existing 3D human methods, showcasing the potential of 3D human representations with integrated biomechanics. We thoroughly evaluate our HFGaussian method against the latest state-of-the-art techniques in human Gaussian splatting and pose estimation, demonstrating its real-time, state-of-the-art performance.

翻译：近年来，辐射场渲染技术的进展在三维场景表示方面展现出有前景的结果，其中基于高斯溅射的技术因其质量和效率而成为最先进的方法。高斯溅射被广泛应用于各种应用，包括三维人体表示。然而，先前的三维高斯溅射方法要么使用参数化人体模型作为附加信息，要么未能提供任何底层结构，如对人体生物力学特征至关重要的信息。本文提出了一种名为HFGaussian的新方法，能够从稀疏输入图像中实时（25 FPS）估计新视角和人体特征，例如三维骨架、三维关键点和密集姿态。所提出的方法利用通用高斯溅射技术来表示人体主体及其相关特征，从而实现高效且通用的重建。通过将姿态回归网络和特征溅射技术与高斯溅射相结合，HFGaussian展示了优于现有三维人体方法的能力，展现了具有集成生物力学的三维人体表示的潜力。我们全面评估了HFGaussian方法，并与人体高斯溅射和姿态估计领域的最新最先进技术进行了比较，证明了其实时、最先进的性能。