Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors
of the multiscale transportation system. Existing methods aim to reconstruct
STTD using low-dimensional models. However, they are limited to data-specific
dimensions or source-dependent patterns, restricting them from unifying
representations. Here, we present a novel paradigm to address the STTD learning
problem by parameterizing STTD as an implicit neural representation. To discern
the underlying dynamics in low-dimensional regimes, coordinate-based neural
networks that can encode high-frequency structures are employed to directly map
coordinates to traffic variables. To unravel the entangled spatial-temporal
interactions, the variability is decomposed into separate processes. We further
enable modeling in irregular spaces such as sensor graphs using spectral
embedding. Through continuous representations, our approach enables the
modeling of a variety of STTD with a unified input, thereby serving as a
generalized learner of the underlying traffic dynamics. It is also shown that
it can learn implicit low-rank priors and smoothness regularization from the
data, making it versatile for learning different dominating data patterns. We
validate its effectiveness through extensive experiments in real-world
scenarios, showcasing applications from corridor to network scales. Empirical
results not only indicate that our model has significant superiority over
conventional low-rank models, but also highlight that the versatility of the
approach extends to different data domains, output resolutions, and network
topologies. Comprehensive model analyses provide further insight into the
inductive bias of STTD. We anticipate that this pioneering modeling perspective
could lay the foundation for universal representation of STTD in various
real-world tasks.

通过参数化空时交通数据（STTD）为隐式神经表示，我们提供了一种新的方法来解决 STTD 学习问题。通过在低维度范围内识别潜在动力学和分解空时交互作用，我们的方法能够进行各种 STTD 的建模，并展示了其在实际应用中的有效性。

时空隐式神经表示作为广义交通数据学习器

Spatiotemporal Implicit Neural Representation as a Generalized Traffic  Data Learner

Neural fields, coordinate-based neural networks, have recently gained
popularity for implicitly representing a scene. In contrast to classical
methods that are based on explicit representations such as point clouds, neural
fields provide a continuous scene representation able to represent 3D geometry
and appearance in a way which is compact and ideal for robotics applications.
However, limited prior methods have investigated registering multiple neural
fields by directly utilising these continuous implicit representations. In this
paper, we present Reg-NF, a neural fields-based registration that optimises for
the relative 6-DoF transformation between two arbitrary neural fields, even if
those two fields have different scale factors. Key components of Reg-NF include
a bidirectional registration loss, multi-view surface sampling, and utilisation
of volumetric signed distance functions (SDFs). We showcase our approach on a
new neural field dataset for evaluating registration problems. We provide an
exhaustive set of experiments and ablation studies to identify the performance
of our approach, while also discussing limitations to provide future direction
to the research community on open challenges in utilizing neural fields in
unconstrained environments.

本研究采用神经场进行注册，通过优化两个具有不同缩放因子的任意神经场之间的相对 6 自由度变换，实现连续隐式表示的多个神经场的注册，同时讨论神经场在无约束环境中的应用挑战和未来研究方向。

Reg-NF: 神经场中隐性表面的高效配准

Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields

Face morphing is one of the seminal problems in computer graphics, with
numerous artistic and forensic applications. It is notoriously challenging due
to pose, lighting, gender, and ethnicity variations. Generally, this task
consists of a warping for feature alignment and a blending for a seamless
transition between the warped images.
We propose to leverage coordinate-based neural networks to represent such
warpings and blendings of face images. During training, we exploit the
smoothness and flexibility of such networks, by combining energy functionals
employed in classical approaches without discretizations. Additionally, our
method is time-dependent, allowing a continuous warping, and blending of the
target images.
During warping inference, we need both direct and inverse transformations of
the time-dependent warping. The first is responsible for morphing the target
image into the source image, while the inverse is used for morphing in the
opposite direction. Our neural warping stores those maps in a single network
due to its inversible property, dismissing the hard task of inverting them.
The results of our experiments indicate that our method is competitive with
both classical and data-based neural techniques under the lens of face-morphing
detection approaches. Aesthetically, the resulting images present a seamless
blending of diverse faces not yet usual in the literature.

我们提出利用基于坐标的神经网络来表示面部图像的变形和融合。通过结合经典方法中的能量函数，我们利用这些网络的平滑性和灵活性进行训练，同时允许连续的面部变形和融合。实验结果表明，我们的方法在面部变形检测方法方面与传统方法和基于数据的神经技术相竞争，并在审美上呈现出文献中尚不常见的多样面孔的无缝融合。

人脸图像的神经隐式变形

Neural Implicit Morphing of Face Images

Numerical integration is a foundational technique in scientific computing and
is at the core of many computer vision applications. Among these applications,
neural volume rendering has recently been proposed as a new paradigm for view
synthesis, achieving photorealistic image quality. However, a fundamental
obstacle to making these methods practical is the extreme computational and
memory requirements caused by the required volume integrations along the
rendered rays during training and inference. Millions of rays, each requiring
hundreds of forward passes through a neural network are needed to approximate
those integrations with Monte Carlo sampling. Here, we propose automatic
integration, a new framework for learning efficient, closed-form solutions to
integrals using coordinate-based neural networks. For training, we instantiate
the computational graph corresponding to the derivative of the network. The
graph is fitted to the signal to integrate. After optimization, we reassemble
the graph to obtain a network that represents the antiderivative. By the
fundamental theorem of calculus, this enables the calculation of any definite
integral in two evaluations of the network. Applying this approach to neural
rendering, we improve a tradeoff between rendering speed and image quality:
improving render times by greater than 10 times with a tradeoff of slightly
reduced image quality.

提出了一种使用基于坐标的神经网络学习高效闭合解法的自动积分框架，在神经渲染中提高渲染速度并改善图像质量。