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

In specific scenarios, face sketch can be used to identify a person. However,
drawing a face sketch often requires exceptional skill and is time-consuming,
limiting its widespread applications in actual scenarios. The new framework of
sketch less face image retrieval (SLFIR)[1] attempts to overcome the barriers
by providing a means for humans and machines to interact during the drawing
process. Considering SLFIR problem, there is a large gap between a partial
sketch with few strokes and any whole face photo, resulting in poor performance
at the early stages. In this study, we propose a multigranularity (MG)
representation learning (MGRL) method to address the SLFIR problem, in which we
learn the representation of different granularity regions for a partial sketch,
and then, by combining all MG regions of the sketches and images, the final
distance was determined. In the experiments, our method outperformed
state-of-the-art baselines in terms of early retrieval on two accessible
datasets. Codes are available at this https URL

通过多粒度表示学习方法（MGRL），该研究提出一种解决半身轮廓图检索（SLFIR）问题的框架，从而在早期获取方面超过了现有基线模型。