基于分层表面体的高效三维关节人体生成
Efficient 3D Articulated Human Generation with Layered Surface Volumes
AI总结：
本文提出分层表面体（LSVs）表示，用模板周围多层带纹理网格高效表达三维关节人体，并结合二维生成器合成各层RGBA纹理，在单视角二维数据上训练出高质量、视角一致的三维人体生成模型LSV-GAN。
摘要：
获取高质量且多样化的三维关节数字人体资产在从虚拟现实到社交平台等各种应用中至关重要。生成式方法，例如三维生成对抗网络（GANs），正在迅速取代费力的人工内容创作工具。然而，现有的三维GAN框架通常依赖的场景表示要么利用模板网格——速度快但质量有限，要么利用体素——容量高但渲染缓慢，从而限制了GAN设置下的三维保真度。在本工作中，我们引入分层表面体（Layered Surface Volumes，LSVs）作为一种面向关节数字人体的新三维对象表示。LSVs使用围绕传统模板的多个带纹理网格层来表示人体。这些层通过alpha合成和快速可微光栅化进行渲染，并且可以被解释为一种体表示，将其容量分配到模板周围有限厚度的流形上。与难以表示头发或配饰等精细离面细节的传统单层模板不同，我们的表面体能够自然地捕获此类细节。LSVs可以被驱动摆姿势，并且在GAN设置中表现出卓越的效率，其中二维生成器学习为各个层合成RGBA纹理。在非结构化的单视角二维图像数据集上训练后，我们的LSV-GAN生成了高质量且视角一致的三维关节数字人体，而无需视角不一致的二维上采样网络。
英文摘要：
Access to high-quality and diverse 3D articulated digital human assets is crucial in various applications, ranging from virtual reality to social platforms. Generative approaches, such as 3D generative adversarial networks (GANs), are rapidly replacing laborious manual content creation tools. However, existing 3D GAN frameworks typically rely on scene representations that leverage either template meshes, which are fast but offer limited quality, or volumes, which offer high capacity but are slow to render, thereby limiting the 3D fidelity in GAN settings. In this work, we introduce layered surface volumes (LSVs) as a new 3D object representation for articulated digital humans. LSVs represent a human body using multiple textured mesh layers around a conventional template. These layers are rendered using alpha compositing with fast differentiable rasterization, and they can be interpreted as a volumetric representation that allocates its capacity to a manifold of finite thickness around the template. Unlike conventional single-layer templates that struggle with representing fine off-surface details like hair or accessories, our surface volumes naturally capture such details. LSVs can be articulated, and they exhibit exceptional efficiency in GAN settings, where a 2D generator learns to synthesize the RGBA textures for the individual layers. Trained on unstructured, single-view 2D image datasets, our LSV-GAN generates high-quality and view-consistent 3D articulated digital humans without the need for view-inconsistent 2D upsampling networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
