Unsupervised generation of 3D-aware clothed humans with various appearances
and controllable geometries is important for creating virtual human avatars and
other AR/VR applications. Existing methods are either limited to rigid object
modeling, or not generative and thus unable to generate high-quality virtual
humans and animate them. In this work, we propose AvatarGen, the first method
that enables not only geometry-aware clothed human synthesis with high-fidelity
appearances but also disentangled human animation controllability, while only
requiring 2D images for training. Specifically, we decompose the generative 3D
human synthesis into pose-guided mapping and canonical representation with
predefined human pose and shape, such that the canonical representation can be
explicitly driven to different poses and shapes with the guidance of a 3D
parametric human model SMPL. AvatarGen further introduces a deformation network
to learn non-rigid deformations for modeling fine-grained geometric details and
pose-dependent dynamics. To improve the geometry quality of the generated human
avatars, it leverages the signed distance field as geometric proxy, which
allows more direct regularization from the 3D geometric priors of SMPL.
Benefiting from these designs, our method can generate animatable 3D human
avatars with high-quality appearance and geometry modeling, significantly
outperforming previous 3D GANs. Furthermore, it is competent for many
applications, e.g., single-view reconstruction, re-animation, and text-guided
synthesis/editing. Code and pre-trained model will be available at
this http URL

本文提出 AvatarGen 方法，是第一种通过仅使用 2D 图像训练，能够生成高保真度外观和可控几何形状的、解耦式的可控人体动画的方法。

AvatarGen：可动漫人类角色的三维生成模型

AvatarGen: A 3D Generative Model for Animatable Human Avatars

Unsupervised generation of high-quality multi-view-consistent images and 3D
shapes using only collections of single-view 2D photographs has been a
long-standing challenge. Existing 3D GANs are either compute-intensive or make
approximations that are not 3D-consistent; the former limits quality and
resolution of the generated images and the latter adversely affects multi-view
consistency and shape quality. In this work, we improve the computational
efficiency and image quality of 3D GANs without overly relying on these
approximations. We introduce an expressive hybrid explicit-implicit network
architecture that, together with other design choices, synthesizes not only
high-resolution multi-view-consistent images in real time but also produces
high-quality 3D geometry. By decoupling feature generation and neural
rendering, our framework is able to leverage state-of-the-art 2D CNN
generators, such as StyleGAN2, and inherit their efficiency and expressiveness.
We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats,
among other experiments.

使用单视角 2D 照片集合进行高质量多视角一致的图片和 3D 形状的非监督生成一直是一个难题，本文提出了一种混合显式 - 隐式网络架构，用于提高 3D GANs 的计算效率和图像质量，实现了高分辨率实时多视角一致的图像合成，同时产生高质量的 3D 几何体。

高效几何感知的三维生成对抗网络

Efficient Geometry-aware 3D Generative Adversarial Networks

The difficulty of textual style transfer lies in the lack of parallel
corpora. Numerous advances have been proposed for the unsupervised generation.
However, significant problems remain with the auto-evaluation of style transfer
tasks. Based on the summary of Pang and Gimpel (2018) and Mir et al. (2019),
style transfer evaluations rely on three criteria: style accuracy of
transferred sentences, content similarity between original and transferred
sentences, and fluency of transferred sentences. We elucidate the problematic
current state of style transfer research. Given that current tasks do not
represent real use cases of style transfer, current auto-evaluation approach is
flawed. This discussion aims to bring researchers to think about the future of
style transfer and style transfer evaluation research.

本文讨论了文本风格迁移技术中的关键问题，即使用无监督生成方法，自动评估风格迁移任务的难点。通过对类似文献的总结，我们阐述了当前风格迁移研究的问题，并指出现有的自动评估方法存在缺陷，无法准确评估迁移后句子的风格准确性、内容相似度和流畅性。本文旨在引导研究者思考风格迁移和评估研究的未来趋势。