We propose SplatArmor, a novel approach for recovering detailed and
animatable human models by `armoring' a parameterized body model with 3D
Gaussians. Our approach represents the human as a set of 3D Gaussians within a
canonical space, whose articulation is defined by extending the skinning of the
underlying SMPL geometry to arbitrary locations in the canonical space. To
account for pose-dependent effects, we introduce a SE(3) field, which allows us
to capture both the location and anisotropy of the Gaussians. Furthermore, we
propose the use of a neural color field to provide color regularization and 3D
supervision for the precise positioning of these Gaussians. We show that
Gaussian splatting provides an interesting alternative to neural rendering
based methods by leverging a rasterization primitive without facing any of the
non-differentiability and optimization challenges typically faced in such
approaches. The rasterization paradigms allows us to leverage forward skinning,
and does not suffer from the ambiguities associated with inverse skinning and
warping. We show compelling results on the ZJU MoCap and People Snapshot
datasets, which underscore the effectiveness of our method for controllable
human synthesis.

我们提出了一种名为 SplatArmor 的新方法，通过在 3D 高斯函数中将参数化的人体模型 “装甲”，从而恢复出详细的、可动画的人体模型。我们的方法将人体表示为一组位于规范空间内的 3D 高斯函数，其关节扭曲通过在规范空间内任意位置扩展底层 SMPL 几何体的蒙皮来定义。为了考虑姿势相关效果，我们引入了一个 SE (3) 场，允许捕捉高斯函数的位置和各向异性。此外，我们提出了使用神经颜色场来提供颜色规范化和用于准确定位这些高斯函数的 3D 监督。我们通过展示 ZJU MoCap 和 People Snapshot 数据集上的令人信服的结果，证明了高斯函数散射提供了一种有趣的替代于基于神经渲染的方法，它利用了不可区分性和优化挑战，而这些挑战通常在这类方法中面临。粗放复刻范例使我们能够利用向前蒙皮，并且不会受到逆蒙皮和扭曲所带来的不确定性的影响。我们的方法对于可控的人体合成的有效性进行了强有力的证明。