We present En3D, an enhanced generative scheme for sculpting high-quality 3D
human avatars. Unlike previous works that rely on scarce 3D datasets or limited
2D collections with imbalanced viewing angles and imprecise pose priors, our
approach aims to develop a zero-shot 3D generative scheme capable of producing
visually realistic, geometrically accurate and content-wise diverse 3D humans
without relying on pre-existing 3D or 2D assets. To address this challenge, we
introduce a meticulously crafted workflow that implements accurate physical
modeling to learn the enhanced 3D generative model from synthetic 2D data.
During inference, we integrate optimization modules to bridge the gap between
realistic appearances and coarse 3D shapes. Specifically, En3D comprises three
modules: a 3D generator that accurately models generalizable 3D humans with
realistic appearance from synthesized balanced, diverse, and structured human
images; a geometry sculptor that enhances shape quality using multi-view normal
constraints for intricate human anatomy; and a texturing module that
disentangles explicit texture maps with fidelity and editability, leveraging
semantical UV partitioning and a differentiable rasterizer. Experimental
results show that our approach significantly outperforms prior works in terms
of image quality, geometry accuracy and content diversity. We also showcase the
applicability of our generated avatars for animation and editing, as well as
the scalability of our approach for content-style free adaptation.

通过以人为基础的准确物理建模，我们提出了一种增强的生成方案 En3D，能够从合成的平衡、多样化和结构化的人类图像中，准确地模拟具有逼真外观的通用化 3D 人物。

En3D：从 2D 合成数据雕刻 3D 人体的增强生成模型

En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D  Synthetic Data

Earth observation from satellite sensory data poses challenging problems,
where machine learning is currently a key player. In recent years, Gaussian
Process (GP) regression has excelled in biophysical parameter estimation tasks
from airborne and satellite observations. GP regression is based on solid
Bayesian statistics and generally yields efficient and accurate parameter
estimates. However, GPs are typically used for inverse modeling based on
concurrent observations and in situ measurements only. Very often a forward
model encoding the well-understood physical relations between the state vector
and the radiance observations is available though and could be useful to
improve predictions and understanding. In this work, we review three GP models
that respect and learn the physics of the underlying processes in the context
of both forward and inverse modeling. After reviewing the traditional
application of GPs for parameter retrieval, we introduce a Joint GP (JGP) model
that combines in situ measurements and simulated data in a single GP model.
Then, we present a latent force model (LFM) for GP modeling that encodes
ordinary differential equations to blend data-driven modeling and physical
constraints of the system governing equations. The LFM performs multi-output
regression, adapts to the signal characteristics, is able to cope with missing
data in the time series, and provides explicit latent functions that allow
system analysis and evaluation. Finally, we present an Automatic Gaussian
Process Emulator (AGAPE) that approximates the forward physical model using
concepts from Bayesian optimization and at the same time builds an optimally
compact look-up-table for inversion. We give empirical evidence of the
performance of these models through illustrative examples of vegetation
monitoring and atmospheric modeling.

本文综述了在地球卫星观测中，机器学习算法（尤其是高斯过程回归）在生物物理参数估计方面的应用，以及如何利用前向模型改进预测和理解物理关系。最后，本文提出了三种高斯过程模型，分别为联合高斯过程模型、潜在力模型和自动高斯过程仿真器，通过植被监测和大气建模的实例验证了它们的性能。