Indoor imaging is a critical task for robotics and internet-of-things. WiFi
as an omnipresent signal is a promising candidate for carrying out passive
imaging and synchronizing the up-to-date information to all connected devices.
This is the first research work to consider WiFi indoor imaging as a
multi-modal image generation task that converts the measured WiFi power into a
high-resolution indoor image. Our proposed WiFi-GEN network achieves a shape
reconstruction accuracy that is 275% of that achieved by physical model-based
inversion methods. Additionally, the Frechet Inception Distance score has been
significantly reduced by 82%. To examine the effectiveness of models for this
task, the first large-scale dataset is released containing 80,000 pairs of WiFi
signal and imaging target. Our model absorbs challenges for the model-based
methods including the non-linearity, ill-posedness and non-certainty into
massive parameters of our generative AI network. The network is also designed
to best fit measured WiFi signals and the desired imaging output. For
reproducibility, we will release the data and code upon acceptance.

WiFi 作为无处不在的信号是进行室内成像和同步最新信息给所有连接设备的一个有前景的选择。该研究工作首次将 WiFi 室内成像视为一个多模式图像生成任务，将测量得到的 WiFi 功率转换为高分辨率的室内图像。我们提出的 WiFi-GEN 网络实现的形状重构准确性是物理模型反演方法的 275％，此外，Frechet Inception Distance 得分减少了 82％。为了检验模型在这个任务中的有效性，我们发布了第一个大规模数据集，其中包含了 8 万对 WiFi 信号和成像目标。我们的模型将非线性、病态以及不确定性等挑战吸收到我们的生成 AI 网络的大量参数中，并且还设计了最佳拟合测得的 WiFi 信号和所需的成像输出。为了可复现性，我们将在接受后发布数据和代码。

AI 助力的 WiFi 室内成像革命

Vision Reimagined: AI-Powered Breakthroughs in WiFi Indoor Imaging

The fashion industry has diverse applications in multi-modal image generation
and editing. It aims to create a desired high-fidelity image with the
multi-modal conditional signal as guidance. Most existing methods learn
different condition guidance controls by introducing extra models or ignoring
the style prior knowledge, which is difficult to handle multiple signal
combinations and faces a low-fidelity problem. In this paper, we adapt both
style prior knowledge and flexibility of multi-modal control into one unified
two-stage framework, M6-Fashion, focusing on the practical AI-aided Fashion
design. It decouples style codes in both spatial and semantic dimensions to
guarantee high-fidelity image generation in the first stage. M6-Fashion
utilizes self-correction for the non-autoregressive generation to improve
inference speed, enhance holistic consistency, and support various signal
controls. Extensive experiments on a large-scale clothing dataset M2C-Fashion
demonstrate superior performances on various image generation and editing
tasks. M6-Fashion model serves as a highly potential AI designer for the
fashion industry.

本文提出了一种统一的两阶段框架 ——M6-Fashion，将样式先验知识和多模态控制的灵活性融合在一起，实现时尚设计中的实用 AI 辅助，取得了优秀的成果。

M6-Fashion：高保真多模态图像生成与编辑

M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing

In this work, we propose TediGAN, a novel framework for multi-modal image
generation and manipulation with textual descriptions. The proposed method
consists of three components: StyleGAN inversion module, visual-linguistic
similarity learning, and instance-level optimization. The inversion module maps
real images to the latent space of a well-trained StyleGAN. The
visual-linguistic similarity learns the text-image matching by mapping the
image and text into a common embedding space. The instance-level optimization
is for identity preservation in manipulation. Our model can produce diverse and
high-quality images with an unprecedented resolution at 1024. Using a control
mechanism based on style-mixing, our TediGAN inherently supports image
synthesis with multi-modal inputs, such as sketches or semantic labels, with or
without instance guidance. To facilitate text-guided multi-modal synthesis, we
propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real
face images and corresponding semantic segmentation map, sketch, and textual
descriptions. Extensive experiments on the introduced dataset demonstrate the
superior performance of our proposed method. Code and data are available at
this https URL

本文提出 TediGAN 框架，用于多模态图像生成和操作。该方法包括三个组件：StyleGAN 反演模块，视觉 - 语言相似度学习和实例级优化。模型可使用多模态输入生成多样化，分辨率为 1024 的高质量图像，并提出 Multi-Modal CelebA-HQ 数据集以支持文本引导的多模态综合。经过广泛实验，本方法展现出优越性能。