PixelGen: Rethinking Embedded Camera Systems
Not on the map. Why?
Abstract (arXiv)
Embedded camera systems are ubiquitous, representing the most widely deployed example of a wireless embedded system. They capture a representation of the world - the surroundings illuminated by visible or infrared light. Despite their widespread usage, the architecture of embedded camera systems has remained unchanged, which leads to limitations. They visualize only a tiny portion of the world. Additionally, they are energy-intensive, leading to limited battery lifespan. We present PixelGen, which re-imagines embedded camera systems. Specifically, PixelGen combines sensors, transceivers, and low-resolution image and infrared vision sensors to capture a broader world representation. They are deliberately chosen for their simplicity, low bitrate, and power consumption, culminating in an energy-efficient platform. We show that despite the simplicity, the captured data can be processed using transformer-based image and language models to generate novel representations of the environment. For example, we demonstrate that it can allow the generation of high-definition images, while the camera utilises low-power, low-resolution monochrome cameras. Furthermore, the capabilities of PixelGen extend beyond traditional photography, enabling visualization of phenomena invisible to conventional cameras, such as sound waves. PixelGen can enable numerous novel applications, and we demonstrate that it enables unique visualization of the surroundings that are then projected on extended reality headsets. We believe, PixelGen goes beyond conventional cameras and opens new avenues for research and photography.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 29 references that are in the map. First posted on arXiv 4 Feb 2024.
Builds on
3 of 29 references.
References · 3
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Offline Ambient-Controlled Latent Diffusion: Architecture, Telemetry, and On-Device Evaluation
- 2025Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation
- 2026RawGen: Learning Camera Raw Image Generation
- 2026SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
- 2026PixelGen: Improving Pixel Diffusion with Perceptual Supervision
- 2026Video Generation Models Are Inherent Lighting Estimators
- 2026SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction
- 2024Enhanced Vision-Language Models for Diverse Sensor Understanding: Cost-Efficient Optimization and Benchmarking