High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model

• URL: http://arxiv.org/abs/2506.10605v2
• Date: Fri, 04 Jul 2025 12:27:28 GMT
• Title: High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model
• Authors: Eshan Ramesh, Takayuki Nishio,
• Abstract summary: We present LatentCSI, a novel method for generating images of the physical environment from WiFi CSI measurements.<n>Our method employs a lightweight neural network to map CSI amplitudes directly into the latent space of an LDM.<n>We then apply the LDM's denoising diffusion model to the latent representation with text-based guidance before decoding.
• Score: 2.847466645223566
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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