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.

翻译：从文本描述创建和定制三维穿衣虚拟人是一项关键且具有挑战性的任务。传统方法通常将人体与服装视为不可分割的整体，限制了用户自由搭配服装的能力。针对这一局限，我们提出分层高斯虚拟人（LAGA），这是一个精心设计的框架，能够生成具有多样服装的高保真可分解放置虚拟人。通过将服装与虚拟人解耦，我们的框架使用户能够在服装层面便捷地编辑虚拟人。我们的方法首先利用按分层结构组织的高斯点集合对虚拟人进行建模，其中每一层对应特定服装或人体本身。为生成各层的高质量服装，我们引入了一种从粗到精的多样化服装生成策略，以及一种新颖的双向SDS损失函数，以维持生成服装与虚拟人组件（包括人体及其他服装）之间的一致性。此外，我们引入三种正则化损失来引导高斯点移动以实现服装迁移，使服装能够自由迁移至不同虚拟人。大量实验证明，我们的方法在三维穿衣人体生成方面超越了现有方法。