The recent advances in text and image synthesis show a great promise for the
future of generative models in creative fields. However, a less explored area
is the one of 3D model generation, with a lot of potential applications to game
design, video production, and physical product design. In our paper, we present
3DGEN, a model that leverages the recent work on both Neural Radiance Fields
for object reconstruction and GAN-based image generation. We show that the
proposed architecture can generate plausible meshes for objects of the same
category as the training images and compare the resulting meshes with the
state-of-the-art baselines, leading to visible uplifts in generation quality.

我们提出了 3DGEN 模型，它利用最近在物体重建和基于 GAN 的图像生成方面的工作，可以为训练图像的同一类别对象生成可信的三维网格，并将生成的网格与当前最先进的基准模型进行比较，取得了可见的生成质量改进。

3DGEN：一种基于 GAN 的从图像数据生成新的 3D 模型的方法

3DGEN: A GAN-based approach for generating novel 3D models from image  data

In recent years, generative adversarial network (GAN)-based image generation
techniques design their generators by stacking up multiple residual blocks. The
residual block generally contains a shortcut, \ie skip connection, which
effectively supports information propagation in the network. In this paper, we
propose a novel shortcut method, called the gated shortcut, which not only
embraces the strength point of the residual block but also further boosts the
GAN performance. More specifically, based on the gating mechanism, the proposed
method leads the residual block to keep (or remove) information that is
relevant (or irrelevant) to the image being generated. To demonstrate that the
proposed method brings significant improvements in the GAN performance, this
paper provides extensive experimental results on the various standard datasets
such as CIFAR-10, CIFAR-100, LSUN, and tiny-ImageNet. Quantitative evaluations
show that the gated shortcut achieves the impressive GAN performance in terms
of Frechet inception distance (FID) and Inception score (IS). For instance, the
proposed method improves the FID and IS scores on the tiny-ImageNet dataset
from 35.13 to 27.90 and 20.23 to 23.42, respectively.

本文介绍了一种新的快捷方式方法 ——gated shortcut，该方法在保留残差块的优点的同时，进一步提高了 GAN 性能。在 CIFAR-10、CIFAR-100、LSUN 和 tiny-ImageNet 等标准数据集上进行的大量实验结果表明，gated shortcut 取得了令人印象深刻的 GAN 性能，如 Frechet inception distance（FID）和 Inception score（IS）。

GAN 的有效快捷技术

Effective Shortcut Technique for GAN

At this moment, GAN-based image generation methods are still imperfect, whose
upsampling design has limitations in leaving some certain artifact patterns in
the synthesized image. Such artifact patterns can be easily exploited (by
recent methods) for difference detection of real and GAN-synthesized images.
However, the existing detection methods put much emphasis on the artifact
patterns, which can become futile if such artifact patterns were reduced.
Towards reducing the artifacts in the synthesized images, in this paper, we
devise a simple yet powerful approach termed FakePolisher that performs shallow
reconstruction of fake images through a learned linear dictionary, intending to
effectively and efficiently reduce the artifacts introduced during image
synthesis. The comprehensive evaluation on 3 state-of-the-art DeepFake
detection methods and fake images generated by 16 popular GAN-based fake image
generation techniques, demonstrates the effectiveness of our technique.Overall,
through reducing artifact patterns, our technique significantly reduces the
accuracy of the 3 state-of-the-art fake image detection methods, i.e., 47% on
average and up to 93% in the worst case.

本篇文章提出了一种通过浅层重构假图像的方式，称为 FakePolisher，旨在通过学习的线性字典有效地减少图像合成过程中引入的伪影模式，从而显著降低 3 种先进的伪造图像检测方法的准确性。