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

Existing efforts on text synthesis for code-switching mostly require training
on code-switched texts in the target language pairs, limiting the deployment of
the models to cases lacking code-switched data. In this work, we study the
problem of synthesizing code-switched texts for language pairs absent from the
training data. We introduce GLOSS, a model built on top of a pre-trained
multilingual machine translation model (PMMTM) with an additional
code-switching module. This module, either an adapter or extra prefixes, learns
code-switching patterns from code-switched data during training, while the
primary component of GLOSS, i.e., the PMMTM, is frozen. The design of only
adjusting the code-switching module prevents our model from overfitting to the
constrained training data for code-switching. Hence, GLOSS exhibits the ability
to generalize and synthesize code-switched texts across a broader spectrum of
language pairs. Additionally, we develop a self-training algorithm on target
language pairs further to enhance the reliability of GLOSS. Automatic
evaluations on four language pairs show that GLOSS achieves at least 55%
relative BLEU and METEOR scores improvements compared to strong baselines.
Human evaluations on two language pairs further validate the success of GLOSS.

GLOSS 是一种用于语言对的代码交换文本综合的模型，它通过自适应器或额外前缀从代码交换数据中学习代码交换模式，该模型构建在预训练的多语言机器翻译模型（PMMTM）之上，它具有自我训练算法进一步提高 GLOSS 的可靠性，并且对四种语言对进行的自动评估表明：与强基线相比，GLOSS BLEU 值和 METEOR 得分至少提高了 55％，同时对两个语言对的人为评估进一步验证了 GLOSS 的成功。