We present DiffHuman, a probabilistic method for photorealistic 3D human
reconstruction from a single RGB image. Despite the ill-posed nature of this
problem, most methods are deterministic and output a single solution, often
resulting in a lack of geometric detail and blurriness in unseen or uncertain
regions. In contrast, DiffHuman predicts a probability distribution over 3D
reconstructions conditioned on an input 2D image, which allows us to sample
multiple detailed 3D avatars that are consistent with the image. DiffHuman is
implemented as a conditional diffusion model that denoises pixel-aligned 2D
observations of an underlying 3D shape representation. During inference, we may
sample 3D avatars by iteratively denoising 2D renders of the predicted 3D
representation. Furthermore, we introduce a generator neural network that
approximates rendering with considerably reduced runtime (55x speed up),
resulting in a novel dual-branch diffusion framework. Our experiments show that
DiffHuman can produce diverse and detailed reconstructions for the parts of the
person that are unseen or uncertain in the input image, while remaining
competitive with the state-of-the-art when reconstructing visible surfaces.

DiffHuman 是一种基于概率的方法，通过一个单一的 RGB 图像实现逼真的三维人体重建。与确定性方法不同，DiffHuman 根据二维图像预测三维重建的概率分布，可以生成与图像一致的多个详细的三维人体模型。此外，DiffHuman 还引入了一个生成器神经网络，可以以大幅减少的运行时间进行渲染，从而实现了一种新颖的双分支扩散框架。在实验中，DiffHuman 在重建可见表面方面与现有技术不相上下，并且可以对输入图像中未见或不确定的部分生成多样且详细的重建结果。

DiffHuman：人体的概率光真实三维重建

DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans

Images perturbed subtly to be misclassified by neural networks, called
adversarial examples, have emerged as a technically deep challenge and an
important concern for several application domains. Most research on adversarial
examples takes as its only constraint that the perturbed images are similar to
the originals. However, real-world application of these ideas often requires
the examples to satisfy additional objectives, which are typically enforced
through custom modifications of the perturbation process. In this paper, we
propose adversarial generative nets (AGNs), a general methodology to train a
generator neural network to emit adversarial examples satisfying desired
objectives. We demonstrate the ability of AGNs to accommodate a wide range of
objectives, including imprecise ones difficult to model, in two application
domains. In particular, we demonstrate physical adversarial examples---eyeglass
frames designed to fool face recognition---with better robustness,
inconspicuousness, and scalability than previous approaches, as well as a new
attack to fool a handwritten-digit classifier.

本文提出了一种通用方法 - 对抗生成网络（AGNs），通过训练一个生成器神经网络输出满足特定目标的对抗样本来解决扰动图像误分类的问题，并在两个应用领域进行了演示。