Abstract
We present LatentCSI, a novel method for generating images of the physical environment from WiFi CSI measurements that leverages a pretrained latent diffusion model (LDM). Unlike prior approaches that rely on complex and computationally intensive techniques such as GANs, our method employs a lightweight neural network to map CSI amplitudes directly into the latent space of an LDM. We then apply the LDM's denoising diffusion model to the latent representation with text-based guidance before decoding using the LDM's pretrained decoder to obtain a high-resolution image. This design bypasses the challenges of pixel-space image generation and avoids the explicit image encoding stage typically required in conventional image-to-image pipelines, enabling efficient and high-quality image synthesis. We validate our approach on two datasets: a wide-band CSI dataset we collected with off-the-shelf WiFi devices and cameras; and a subset of the publicly available MM-Fi dataset. The results demonstrate that LatentCSI outperforms baselines of comparable complexity trained directly on ground-truth images in both computational efficiency and perceptual quality, while additionally providing practical advantages through its unique capacity for text-guided controllability.
Abstract (translated)
我们提出了LatentCSI，这是一种利用预训练的潜在扩散模型（LDM）从WiFi CSI测量生成物理环境图像的新方法。与以往依赖复杂且计算密集的技术如GAN的方法不同，我们的方法采用轻量级神经网络直接将CSI幅度映射到LDM的潜在空间中。接着，我们使用带有文本引导的LDM去噪扩散模型对潜在表示进行处理，并通过预训练解码器解码以获得高分辨率图像。这种设计绕过了像素空间图像生成的挑战，并避免了传统图像到图像工作流通常需要的显式图像编码阶段，从而实现了高效且高质量的图像合成。 我们在两个数据集上验证了我们的方法：一个是使用现成的WiFi设备和摄像头收集的宽带CSI数据集；另一个是公开可用的MM-Fi数据子集。实验结果表明，在计算效率和感知质量方面，LatentCSI优于直接在真实图像上训练的复杂度相当的基础模型，同时通过其独特的文本引导可控性提供了实用优势。
URL
https://arxiv.org/abs/2506.10605
