In this paper, we introduce Neural-ABC, a novel parametric model based on
neural implicit functions that can represent clothed human bodies with
disentangled latent spaces for identity, clothing, shape, and pose. Traditional
mesh-based representations struggle to represent articulated bodies with
clothes due to the diversity of human body shapes and clothing styles, as well
as the complexity of poses. Our proposed model provides a unified framework for
parametric modeling, which can represent the identity, clothing, shape and pose
of the clothed human body. Our proposed approach utilizes the power of neural
implicit functions as the underlying representation and integrates
well-designed structures to meet the necessary requirements. Specifically, we
represent the underlying body as a signed distance function and clothing as an
unsigned distance function, and they can be uniformly represented as unsigned
distance fields. Different types of clothing do not require predefined
topological structures or classifications, and can follow changes in the
underlying body to fit the body. Additionally, we construct poses using a
controllable articulated structure. The model is trained on both open and newly
constructed datasets, and our decoupling strategy is carefully designed to
ensure optimal performance. Our model excels at disentangling clothing and
identity in different shape and poses while preserving the style of the
clothing. We demonstrate that Neural-ABC fits new observations of different
types of clothing. Compared to other state-of-the-art parametric models,
Neural-ABC demonstrates powerful advantages in the reconstruction of clothed
human bodies, as evidenced by fitting raw scans, depth maps and images. We show
that the attributes of the fitted results can be further edited by adjusting
their identities, clothing, shape and pose codes.

本文介绍了 Neural-ABC，一种基于神经隐式函数的新型参数模型，可以使用离散的潜在空间来表示着装的人体，涵盖身份、服装、形状和姿势。该模型通过利用神经隐式函数的优势，并结合精心设计的结构来满足必要的要求，使用有符号距离函数表示基础身体和无符号距离函数表示衣物，具有较强的重建能力并保留了服装的风格。

神经 - ABC：关于有衣物的关节体的神经参数模型

Neural-ABC: Neural Parametric Models for Articulated Body with Clothes

This paper introduces a new architectural design framework that utilizes
generative AI tools including ChatGPT and Veras with parametric modeling and
Building Information Modeling (BIM) to enhance the design process. The study
experiments with the potential of ChatGPT and generative AI in 3D architectural
design, extending beyond its use in text and 2D image generation. The proposed
framework promotes collaboration between architects and AI, facilitating a
quick exploration of design ideas and producing context-sensitive, creative
design generation. By integrating ChatGPT for scripting and Veras for
generating design ideas with widely used parametric modeling and BIM tools, the
framework provides architects with an intuitive and powerful method to convey
design intent, leading to more efficient, creative, and collaborative design
processes.

该研究介绍了一个新的建筑设计框架，利用生成式人工智能工具，包括 ChatGPT 和 Veras，结合参数化建模和建筑信息模型（BIM），提升设计流程。这个框架扩展了 ChatGPT 和生成式人工智能在三维建筑设计中的潜力，不仅限于在文本和二维图像生成方面的应用。通过整合 ChatGPT 用于脚本编写和 Veras 用于生成设计思路，并结合广泛使用的参数化建模和 BIM 工具，该框架为建筑师提供了直观而强大的方法来传达设计意图，从而实现更高效、创造性和协作性的设计流程。

建筑设计中基于生成式人工智能的参数化建模与 BIM 实验

Experiments on Generative AI-Powered Parametric Modeling and BIM for  Architectural Design

This paper presents a novel approach for incremental semiparametric inverse
dynamics learning. In particular, we consider the mixture of two approaches:
Parametric modeling based on rigid body dynamics equations and nonparametric
modeling based on incremental kernel methods, with no prior information on the
mechanical properties of the system. This yields to an incremental
semiparametric approach, leveraging the advantages of both the parametric and
nonparametric models. We validate the proposed technique learning the dynamics
of one arm of the iCub humanoid robot.

本文提出了一种新颖的增量半参数反向动力学学习方法，通过刚体动力学方程的参数建模和增量核方法的非参数建模相结合，实现了对机械系统动力学的增量半参数学习，并在 iCub 人型机器人贴身进行验证。