Is it really possible to see what people in the next room are doing through a wall, just by using your home Wi-Fi signal? Recently, an open-source project called “RuView” claimed that by combining Wi-Fi CSI (Channel State Information) with AI, it could detect human posture, breathing rate, and heart rate through walls, with accuracy reportedly reaching 17 key skeletal landmarks. In a short period of time, its GitHub star count surged past the 50,000 mark. However, before the hype had even subsided, the developer community began a widespread “debunking” campaign, accusing RuView of being a complete and utter hoax.
“Seeing Through Walls” Goes Viral
The concept behind RuView sounds quite like something out of science fiction. According to the development team on GitHub, the system collects data from radio waves emitted by Wi-Fi routers, analyzes minute changes in signal amplitude and phase, and then uses signal processing pipelines and neural networks to infer the presence and posture of a human body. Supported features include: human tracking behind walls, contactless respiration and heart rate monitoring, sleep monitoring, fall detection, and detection of what are referred to as “17 key human posture landmarks.”
恐怖！用家Wi-Fi就能知道隔壁墙后的人在干什么！
开源项目 RuView 在GitHub已经冲到 5万+星，直接炸了！完全不用摄像头，也不用戴任何设备，就靠家里普通的WiFi信号，就能穿墙透视：
– 隔壁有几个人、在哪个位置、是走路还是躺着，全看得见
– 实时看人体姿态（17个关键点）
-… pic.twitter.com/88Z13KU6Gr— 开发者Hailey (@IndieDevHailey) May 13, 2026
The project’s hardware requirements are modest; it can run on an ESP32-S3 microcontroller (about NT$300 per unit) or a professional wireless NIC. The software is processed entirely locally without any cloud involvement, and the official Docker image provides simulated data so users can try it out in advance even without the hardware. The development team emphasizes that this is a “privacy-protecting” solution because it does not use any cameras at all, making it naturally compliant with GDPR regulations.
From Being Adored by Countless Fans to Being Exposed for Fraud
However, dissenting opinions soon began to surface online. After conducting an in-depth investigation, the tech blog Digital Biz Talk publishedInvestigation Report…accusing RuView of being a “sophisticated scam,” claiming that the core of the entire project does not actually work and that all of its “magical” features are fake. The research found that the core algorithm in the main program, rf_tomography.py, is merely an empty placeholder that returns either random noise or pre-recorded animations, and lacks any actual RF data processing capability.
A discussion on Reddit’s r/programming subreddit proved to be a key breakthrough. A developer actually added monitoring instructions to the code and discovered that the so-called “tomography” function was receiving either zeros or static test vectors—not actual RF signals. The output visualization was simply an independent, pre-recorded timer loop—the entire pipeline was fake. Worse still, the academic papers cited by the project were either misapplied or came from fields such as medical imaging that require controlled, high-power equipment, and were completely unrelated to consumer-grade Wi-Fi hardware.
Signal physics tells you: This won’t be possible in 2026
Why do experts say that “seeing through walls” is simply impossible with today’s consumer-grade hardware? RF researchers point out that the signal wavelengths of 2.4GHz and 5GHz Wi-Fi are too long, and the antennas built into standard laptops or routers are not arranged in a synthetic aperture radar array configuration, so the noise floor drowns out all the minute signal changes needed. Wi-Fi can perform basic posture detection and presence sensing, but reconstructing the shape of a human body behind a wall remains the stuff of science fiction compared to what consumer-grade hardware will be capable of in 2026.
How the Scam Works: Complex Disguises and the Inclusion of Unrelated Modules
Digital Biz Talk further revealed that RuView’s scam actually followed a carefully designed scheme. The `requirements.txt` file listed dozens of obscure scientific computing libraries, which not only made the project appear professional and serious but also created the impression that “failed environment setup was the user’s own fault.” At the same time, the developers included several fully functional modules that were completely unrelated to the core functionality—such as a complete Wi-Fi network scanning script and a flashy Plotly interactive charting tool—so that when users ran the project, they would see beautiful charts and mistakenly believe that the “wall-penetrating visualization” feature was working, when in reality, it’s just a canned animation.
Currently, RuView still has a large number of stars on GitHub, but the community has begun calling for a boycott through a petition. The official team has not yet publicly responded to the allegations of data manipulation,GitHub RepositoryIt has not yet been taken down.
Source: KOCPC Chinese