This is my paper (first author).
I think the results here are much less important and surprising than what some people seem to be thinking. To summarize the core of the paper, we took stable diffusion (which is a 3-part system of an encoder, u-net, decoder), and replaced the encoder to use WiFi data instead of images. This gives you two advantages: you get text-based guidance for free, and the encoder model can be smaller. The smaller model combined with the semantic compression from the autoencoder gives you better (SOTA resolution) results, much faster.
I noticed a lot of discussion about how the model can possibly be so accurate. It wouldn't be wrong to consider the model overfit, in the sense that the visual details of the scene are moved from the training data to the model weights. These kinds of models are meant to be trained & deployed in a single environment. What's interesting about this work is that learning the environment well has become really fast because the output dimension is smaller than image space. In fact, it's so fast that you can basically do it in real time... you turn on a data collection node and can train a model from scratch online, in a new environment that gets decent results with at least a little bit of interesting generalization in ~10min. I'm presenting a demonstration of this at Mobicom 2025 next month in Hong Kong.
What people call "WiFi sensing" is now mostly CSI (channel state information) sensing. When you transmit a packet on many subcarriers (frequencies), the CSI represents how the data on each frequency changed during transmission. So, CSI is inherently quite sensitive to environmental changes.
I want to point out something that most everybody working in the CSI sensing/general ISAC space seems to know: generalization is hard and most definitely unsolved for any reasonably high-dimensional sensing problem (like image generation and to some extent pose estimation). I see a lot of fearmongering online about wifi sensing killing privacy for good, but in my opinion we're still quite far off.
I've made the project's code and some formatted data public since this paper is starting to pick up some attention: https://github.com/nishio-laboratory/latentcsi
Is there a survey of SoTA of what can be achieved with CSI sensing you would recommend?
What is available on the low level? Are researchers using SDR, or there are common wifi chips that properly report CSI? Do most people feed in CSI of literally every packet, or is it sampled?
I'd suggest reading https://dl.acm.org/doi/abs/10.1145/3310194 (2019) for a survey on early methods and https://arxiv.org/abs/2503.08008.
As for low level:
The most common early hardware was afaik esp32s & https://stevenmhernandez.github.io/ESP32-CSI-Tool/, and also old intel NICs & https://dhalperi.github.io/linux-80211n-csitool/.
Now many people use https://ps.zpj.io/ which supports some hardware including SDRs, but I must discourage using it, especially for research, as it's not free software and has a restrictive license. I used https://feitcsi.kuskosoft.com/ which uses a slightly modified iwlwifi driver, since iwlwifi needs to compute CSI anyway. There are free software alternatives for SDR CSI extraction as well; it's not hard to build an OFDM chain with GNUradio and extract CSI, although this might require a slightly more in-depth understanding of how wifi works.