Generating face image with specific gaze information has attracted
considerable attention. Existing approaches typically input gaze values
directly for face generation, which is unnatural and requires annotated gaze
datasets for training, thereby limiting its application. In this paper, we
present a novel gaze-controllable face generation task. Our approach inputs
textual descriptions that describe human gaze and head behavior and generates
corresponding face images. Our work first introduces a text-of-gaze dataset
containing over 90k text descriptions spanning a dense distribution of gaze and
head poses. We further propose a gaze-controllable text-to-face method. Our
method contains a sketch-conditioned face diffusion module and a model-based
sketch diffusion module. We define a face sketch based on facial landmarks and
eye segmentation map. The face diffusion module generates face images from the
face sketch, and the sketch diffusion module employs a 3D face model to
generate face sketch from text description. Experiments on the FFHQ dataset
show the effectiveness of our method. We will release our dataset and code for
future research.

本文提出了一种新颖的可控凝视的人脸生成任务，方法使用文本描述输入来生成具有相应凝视特征的人脸图像，并通过面部草图和 3D 人脸模型实现了凝视的文本到人脸的转换。实验证明了方法的有效性，同时作者将提供数据集和代码以供今后研究使用。

TextGaze: 自然语言驱动的可操控注视生成人脸

TextGaze: Gaze-Controllable Face Generation with Natural Language

Diffusion models, which employ stochastic differential equations to sample
images through integrals, have emerged as a dominant class of generative
models. However, the rationality of the diffusion process itself receives
limited attention, leaving the question of whether the problem is well-posed
and well-conditioned. In this paper, we uncover a vexing propensity of
diffusion models: they frequently exhibit the infinite Lipschitz near the zero
point of timesteps. This poses a threat to the stability and accuracy of the
diffusion process, which relies on integral operations. We provide a
comprehensive evaluation of the issue from both theoretical and empirical
perspectives. To address this challenge, we propose a novel approach, dubbed
E-TSDM, which eliminates the Lipschitz singularity of the diffusion model near
zero. Remarkably, our technique yields a substantial improvement in
performance, e.g., on the high-resolution FFHQ dataset ($256\times256$).
Moreover, as a byproduct of our method, we manage to achieve a dramatic
reduction in the Frechet Inception Distance of other acceleration methods
relying on network Lipschitz, including DDIM and DPM-Solver, by over 33$\%$. We
conduct extensive experiments on diverse datasets to validate our theory and
method. Our work not only advances the understanding of the general diffusion
process, but also provides insights for the design of diffusion models.

本文研究了扩散模型在零时刻附近的无穷 Lipschitz 性和该问题对模型稳定性和准确性的影响，提出了一种名为 E-TSDM 的新方法，该方法可以消除扩散模型附近零点处的 Lipschitz 奇异性，并实现了在高分辨率数据集（$256	imes256$）上的大幅提高性能。

消除扩散模型中的利普希茨奇性

Eliminating Lipschitz Singularities in Diffusion Models

We address the problem of single photo age progression and regression-the
prediction of how a person might look in the future, or how they looked in the
past. Most existing aging methods are limited to changing the texture,
overlooking transformations in head shape that occur during the human aging and
growth process. This limits the applicability of previous methods to aging of
adults to slightly older adults, and application of those methods to photos of
children does not produce quality results. We propose a novel multi-domain
image-to-image generative adversarial network architecture, whose learned
latent space models a continuous bi-directional aging process. The network is
trained on the FFHQ dataset, which we labeled for ages, gender, and semantic
segmentation. Fixed age classes are used as anchors to approximate continuous
age transformation. Our framework can predict a full head portrait for ages
0-70 from a single photo, modifying both texture and shape of the head. We
demonstrate results on a wide variety of photos and datasets, and show
significant improvement over the state of the art.

该论文介绍了一种基于多域图像生成对抗网络的单张图片年龄进退展方法，其学习的潜在空间模拟了一个连续的双向老化过程。该框架可从一张照片中预测 0-70 岁的完整头像，改变头部的纹理和形状，这取得了显著的改进。