Creating and animating 3D biped cartoon characters is crucial and valuable in
various applications. Compared with geometry, the diverse texture design plays
an important role in making 3D biped cartoon characters vivid and charming.
Therefore, we focus on automatic texture design for cartoon characters based on
input instructions. This is challenging for domain-specific requirements and a
lack of high-quality data. To address this challenge, we propose Make-It-Vivid,
the first attempt to enable high-quality texture generation from text in UV
space. We prepare a detailed text-texture paired data for 3D characters by
using vision-question-answering agents. Then we customize a pretrained
text-to-image model to generate texture map with template structure while
preserving the natural 2D image knowledge. Furthermore, to enhance fine-grained
details, we propose a novel adversarial learning scheme to shorten the domain
gap between original dataset and realistic texture domain. Extensive
experiments show that our approach outperforms current texture generation
methods, resulting in efficient character texturing and faithful generation
with prompts. Besides, we showcase various applications such as out of domain
generation and texture stylization. We also provide an efficient generation
system for automatic text-guided textured character generation and animation.

基于输入指令，我们提出了第一次尝试在 UV 空间内，通过 Make-It-Vivid 从文本生成高质量纹理的方法，同时利用预训练的文本到图像模型生成具有模板结构的纹理映射，最终通过对抗学习缩小原始数据集和真实纹理领域之间的差距，实现了高效的角色纹理化和忠实的生成。

让其栩栩如生：对双足卡通人物进行从文本装饰

Make-It-Vivid: Dressing Your Animatable Biped Cartoon Characters from  Text

Assisting people in efficiently producing visually plausible 3D characters
has always been a fundamental research topic in computer vision and computer
graphics. Recent learning-based approaches have achieved unprecedented accuracy
and efficiency in the area of 3D real human digitization. However, none of the
prior works focus on modeling 3D biped cartoon characters, which are also in
great demand in gaming and filming. In this paper, we introduce 3DBiCar, the
first large-scale dataset of 3D biped cartoon characters, and RaBit, the
corresponding parametric model. Our dataset contains 1,500 topologically
consistent high-quality 3D textured models which are manually crafted by
professional artists. Built upon the data, RaBit is thus designed with a
SMPL-like linear blend shape model and a StyleGAN-based neural UV-texture
generator, simultaneously expressing the shape, pose, and texture. To
demonstrate the practicality of 3DBiCar and RaBit, various applications are
conducted, including single-view reconstruction, sketch-based modeling, and 3D
cartoon animation. For the single-view reconstruction setting, we find a
straightforward global mapping from input images to the output UV-based texture
maps tends to lose detailed appearances of some local parts (e.g., nose, ears).
Thus, a part-sensitive texture reasoner is adopted to make all important local
areas perceived. Experiments further demonstrate the effectiveness of our
method both qualitatively and quantitatively. 3DBiCar and RaBit are available
at gaplab.cuhk.edu.cn/projects/RaBit.

本文介绍了 3DBiCar 和 RaBit，它们分别是首个大规模的 3D 双足卡通人物数据集和相应的参数化模型。基于数据集，RaBit 采用类似 SMPL 的线性融合形状模型和基于 StyleGAN 的神经 UV 纹理生成器，同时表达形状、姿势和纹理，并在单视图重建、基于草图的建模和 3D 卡通动画等多个应用方面展示了实用性。