Beeble's product page states that PBR, alpha, and depth map generation are "Powered by SwitchLight 3.0." Analysis of the application reveals a more nuanced picture:

Pipeline stage	What Beeble says	What the application contains
Alpha matte (background removal)	"Powered by SwitchLight 3.0"	`transparent-background` / InSPyReNet (MIT)
Depth map	"Powered by SwitchLight 3.0"	Depth Anything V2 via Kornia (Apache 2.0)
Person detection	Not mentioned	RT-DETR + PP-HGNet via Kornia (Apache 2.0)
Face detection	Not mentioned	Kornia face detection (Apache 2.0)
Multi-object tracking	Not mentioned	BoxMOT via Kornia (MIT)
Edge detection	Not mentioned	DexiNed via Kornia (Apache 2.0)
Feature extraction	Not mentioned	DINOv2 via timm (Apache 2.0)
Segmentation	Not mentioned	segmentation_models_pytorch (MIT)
Super resolution	Not mentioned	RRDB-Net via Kornia (Apache 2.0)
PBR decomposition (normal, base color, roughness, specular, metallic)	SwitchLight 3.0	Architecture built on segmentation_models_pytorch + timm backbones (PP-HGNet, ResNet); proprietary trained weights
Relighting	SwitchLight 3.0	Proprietary (not fully characterized)

The preprocessing pipeline--alpha mattes, depth maps, person detection, face detection, multi-object tracking, edge detection, segmentation, and super resolution--is built entirely from open-source models used off the shelf.

The PBR decomposition model, marketed as part of SwitchLight 3.0, appears to be architecturally built from the same open-source encoder-decoder frameworks and pretrained backbones available to anyone. No physics-based rendering code (Cook-Torrance, BRDF, spherical harmonics) was found in the application binary, despite the CVPR 2024 paper describing such an architecture. The proprietary element appears to be the trained weights, not the model architecture.

The name "SwitchLight" does not appear anywhere in the application binary, the setup binary, or the Electron app source code. It is a marketing name with no corresponding software component.

Beeble does acknowledge the use of open-source models in their FAQ: "When open-source models are included, we choose them carefully." However, the product page attributes all outputs to SwitchLight without distinguishing which passes come from open-source components.

Why this matters

Most Beeble Studio users use the application for PBR extractions--alpha mattes, diffuse/albedo, normals and depth maps--not for relighting within the software. The alpha and depth extractions use open-source models directly and can be replicated for free. The PBR extractions use standard open-source architectures with custom-trained weights. Open-source alternatives for PBR decomposition (CHORD, RGB-X) now exist and are narrowing the gap. See the ComfyUI guide for details.

The application bundles approximately 48 Python packages, of which only 6 include license files. All identified open-source components require attribution under their licenses (MIT and Apache 2.0). No attribution was found for core components. See the license analysis.

Documentation

Full report -- Detailed findings with evidence for each identified component and architecture analysis
License analysis -- License requirements and compliance assessment
Methodology -- How the analysis was performed and what was not done
ComfyUI guide -- How to replicate the pipeline with open-source tools
Verification guide -- How to independently verify these findings
Marketing claims -- Archived quotes from Beeble's public pages

Methodology

The analysis combined several non-invasive techniques: string extraction from process memory, TensorRT plugin identification, PyInstaller module listing, Electron app source inspection, library directory inventory, and manifest analysis. No code was decompiled, no encryption was broken, and no proprietary logic was examined. The full methodology is documented here.

What this is

This is a factual technical analysis. The evidence is presented so that VFX professionals can make informed decisions about the tools they use. All claims are verifiable using the methods described in the verification guide.

What this is not

This is not an accusation of wrongdoing. Using open-source software in commercial products is normal, legal, and encouraged by the open-source community. Using open-source architectures with custom training data is how most ML products are built. The concerns documented here are narrower:

Marketing language that attributes open-source outputs to proprietary technology
Investor-facing claims about a "foundational model" that appears to be a pipeline of open-source components with proprietary weights
A CVPR paper describing a physics-based architecture that does not appear to match the deployed application
Missing license attribution required by MIT and Apache 2.0

The first and fourth are correctable. The second is a question for investors. The third is a question for the research community.

License

This repository is licensed under CC BY 4.0.