Manually creating textures for 3D meshes is time-consuming, even for expert
visual content creators. We propose a fast approach for automatically texturing
an input 3D mesh based on a user-provided text prompt. Importantly, our
approach disentangles lighting from surface material/reflectance in the
resulting texture so that the mesh can be properly relit and rendered in any
lighting environment. We introduce LightControlNet, a new text-to-image model
based on the ControlNet architecture, which allows the specification of the
desired lighting as a conditioning image to the model. Our text-to-texture
pipeline then constructs the texture in two stages. The first stage produces a
sparse set of visually consistent reference views of the mesh using
LightControlNet. The second stage applies a texture optimization based on Score
Distillation Sampling (SDS) that works with LightControlNet to increase the
texture quality while disentangling surface material from lighting. Our
pipeline is significantly faster than previous text-to-texture methods, while
producing high-quality and relightable textures.

基于用户提供的文本提示，我们提出了一种快速的自动给输入的 3D 网格上色的方法。我们的方法将光照与表面材质 / 反射分离，以便在任何光照环境中正确照明和渲染网格。我们介绍了一种基于 ControlNet 架构的新的文本到图像模型 LightControlNet，它允许将期望的光照规范为模型的一个条件图像。然后，我们的文本到纹理流程通过两个阶段构建纹理。第一阶段使用 LightControlNet 产生一组稀疏的视觉一致的网格参考视图。第二阶段根据得分蒸馏采样 (SDS) 进行纹理优化，该优化方法与 LightControlNet 配合工作，以提高纹理质量并分离表面材质和光照。我们的流程比先前的文本到纹理方法快得多，同时能产生高质量且可重照的纹理。

FlashTex：快速可再照明网格纹理与 LightControlNet

FlashTex: Fast Relightable Mesh Texturing with LightControlNet

Recent studies reveal that deep neural network (DNN) based object detectors
are vulnerable to adversarial attacks in the form of adding the perturbation to
the images, leading to the wrong output of object detectors. Most current
existing works focus on generating perturbed images, also called adversarial
examples, to fool object detectors. Though the generated adversarial examples
themselves can remain a certain naturalness, most of them can still be easily
observed by human eyes, which limits their further application in the real
world. To alleviate this problem, we propose a differential evolution based
dual adversarial camouflage (DE_DAC) method, composed of two stages to fool
human eyes and object detectors simultaneously. Specifically, we try to obtain
the camouflage texture, which can be rendered over the surface of the object.
In the first stage, we optimize the global texture to minimize the discrepancy
between the rendered object and the scene images, making human eyes difficult
to distinguish. In the second stage, we design three loss functions to optimize
the local texture, making object detectors ineffective. In addition, we
introduce the differential evolution algorithm to search for the near-optimal
areas of the object to attack, improving the adversarial performance under
certain attack area limitations. Besides, we also study the performance of
adaptive DE_DAC, which can be adapted to the environment. Experiments show that
our proposed method could obtain a good trade-off between the fooling human
eyes and object detectors under multiple specific scenes and objects.

本研究提出一种基于差分演化的对抗性伪装方法 (DE_DAC)，旨在同时欺骗人眼和目标检测器。该方法包含两个阶段，分别对全局和局部纹理进行优化，从而达到伪装目的。其中，差分演化算法用于搜索攻击对象的最优区域。实验结果表明，该方法在多个场景和物体上能够良好地平衡人眼和目标检测器的伪装效果。