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 work presents CLOTH3D, the first big scale synthetic dataset of 3D
clothed human sequences. CLOTH3D contains a large variability on garment type,
topology, shape, size, tightness and fabric. Clothes are simulated on top of
thousands of different pose sequences and body shapes, generating realistic
cloth dynamics. We provide the dataset with a generative model for cloth
generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on
graph convolutions (GCVAE) to learn garment latent spaces. This allows for
realistic generation of 3D garments on top of SMPL model for any pose and
shape.

CLOTH3D 是第一份大规模的 3D 服装人体序列的综合性合成数据集，并提供了基于条件变分自编码器和图卷积的生成模型，实现了在任何姿势和形状下逼真生成 3D 服装。