Creating and customizing a 3D clothed avatar from textual descriptions is a
critical and challenging task. Traditional methods often treat the human body
and clothing as inseparable, limiting users' ability to freely mix and match
garments. In response to this limitation, we present LAyered Gaussian Avatar
(LAGA), a carefully designed framework enabling the creation of high-fidelity
decomposable avatars with diverse garments. By decoupling garments from avatar,
our framework empowers users to conviniently edit avatars at the garment level.
Our approach begins by modeling the avatar using a set of Gaussian points
organized in a layered structure, where each layer corresponds to a specific
garment or the human body itself. To generate high-quality garments for each
layer, we introduce a coarse-to-fine strategy for diverse garment generation
and a novel dual-SDS loss function to maintain coherence between the generated
garments and avatar components, including the human body and other garments.
Moreover, we introduce three regularization losses to guide the movement of
Gaussians for garment transfer, allowing garments to be freely transferred to
various avatars. Extensive experimentation demonstrates that our approach
surpasses existing methods in the generation of 3D clothed humans.

从文本描述中创建和定制 3D 着装化身是一个关键且具有挑战性的任务。传统方法通常将人体和服装视为不可分割的，限制了用户自由混搭服装的能力。为了应对这一限制，我们提出了一种精心设计的框架 LAyered Gaussian Avatar（LAGA），它能够创建具有各种服装的高保真分解化身。通过将服装与化身解耦，我们的框架使用户能够在服装级别方便地编辑化身。我们的方法通过使用一组高斯点组织成分层结构来对化身建模，其中每一层对应于特定的服装或人体本身。为了生成每层的高质量服装，我们引入了一种分层生成多样化服装的粗糙到精细的策略，以及一种新的双 SDS 损失函数，以保持生成的服装与化身组件（包括人体和其他服装）之间的一致性。此外，我们引入了三个正则化损失来指导高斯点的运动，实现服装转移，使得服装能够自由地转移到各种化身上。大量实验表明，我们的方法在生成 3D 着装人物方面超过了现有方法。

LAGA: 分层 3D 角色生成与定制

LAGA: Layered 3D Avatar Generation and Customization via Gaussian  Splatting

This paper presents a framework for efficient 3D clothed avatar
reconstruction. By combining the advantages of the high accuracy of
optimization-based methods and the efficiency of learning-based methods, we
propose a coarse-to-fine way to realize a high-fidelity clothed avatar
reconstruction (CAR) from a single image. At the first stage, we use an
implicit model to learn the general shape in the canonical space of a person in
a learning-based way, and at the second stage, we refine the surface detail by
estimating the non-rigid deformation in the posed space in an optimization way.
A hyper-network is utilized to generate a good initialization so that the
convergence o f the optimization process is greatly accelerated. Extensive
experiments on various datasets show that the proposed CAR successfully
produces high-fidelity avatars for arbitrarily clothed humans in real scenes.

本文提出了一个高效的三维服装化身重建框架。该框架通过高精度的基于优化的方法和高效的基于学习的方法相结合，以从单个图像中实现高保真度的服装化身重建为目标。通过在规范空间中以基于学习的方式使用隐式模型来学习人的一般形状，并通过以优化方式在姿态空间中估计非刚性变形来细化表面细节，使用超级网络来生成良好的初始化，从而极大地加速了优化过程的收敛。在各种数据集上的大量实验表明，所提出的服装化身重建框架成功地为现实场景中的任意穿着的人类产生了高保真度的化身。