Parametric 3D models have enabled a wide variety of tasks in computer
graphics and vision, such as modeling human bodies, faces, and hands. However,
the construction of these parametric models is often tedious, as it requires
heavy manual tweaking, and they struggle to represent additional complexity and
details such as wrinkles or clothing. To this end, we propose Neural Parametric
Models (NPMs), a novel, learned alternative to traditional, parametric 3D
models, which does not require hand-crafted, object-specific constraints. In
particular, we learn to disentangle 4D dynamics into latent-space
representations of shape and pose, leveraging the flexibility of recent
developments in learned implicit functions. Crucially, once learned, our neural
parametric models of shape and pose enable optimization over the learned spaces
to fit to new observations, similar to the fitting of a traditional parametric
model, e.g., SMPL. This enables NPMs to achieve a significantly more accurate
and detailed representation of observed deformable sequences. We show that NPMs
improve notably over both parametric and non-parametric state of the art in
reconstruction and tracking of monocular depth sequences of clothed humans and
hands. Latent-space interpolation as well as shape/pose transfer experiments
further demonstrate the usefulness of NPMs. Code is publicly available at
this https URL

本论文提出了一种学习式的 Neural Parametric Models (NPMs) 用于精确地重建和跟踪人体、手部和服装等物体的运动序列，并展示了该方法相对于传统参数化或非参数化方法的优越性。

NPMs: 三维可变形形状的神经参数模型

NPMs: Neural Parametric Models for 3D Deformable Shapes

We study the performance of long short-term memory networks (LSTMs) and
neural ordinary differential equations (NODEs) in learning latent-space
representations of dynamical equations for an advection-dominated problem given
by the viscous Burgers equation. Our formulation is devised in a non-intrusive
manner with an equation-free evolution of dynamics in a reduced space with the
latter being obtained through a proper orthogonal decomposition. In addition,
we leverage the sequential nature of learning for both LSTMs and NODEs to
demonstrate their capability for closure in systems which are not completely
resolved in the reduced space. We assess our hypothesis for two
advection-dominated problems given by the viscous Burgers equation. It is
observed that both LSTMs and NODEs are able to reproduce the effects of the
absent scales for our test cases more effectively than intrusive dynamics
evolution through a Galerkin projection. This result empirically suggests that
time-series learning techniques implicitly leverage a memory kernel for
coarse-grained system closure as is suggested through the Mori-Zwanzig
formalism.

研究了 LSTM 和 NODE 在学习动态微分方程潜在空间表示方面的性能，并通过两个粘性 Burgers 方程测试验证了其在系统封闭方面的能力。