beeble-forensic-analysis/README.md

# Beeble Studio: Technical Analysis

An independent technical analysis of the Beeble Studio desktop
application, examining which AI models power its Video-to-VFX
pipeline.


## Findings

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](https://docs.beeble.ai/help/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](docs/COMFYUI_GUIDE.md) 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](docs/LICENSE_ANALYSIS.md).


## Documentation

- **[Full report](docs/REPORT.md)** -- Detailed findings with
  evidence for each identified component and architecture analysis
- **[License analysis](docs/LICENSE_ANALYSIS.md)** -- License
  requirements and compliance assessment
- **[Methodology](docs/METHODOLOGY.md)** -- How the analysis was
  performed and what was not done
- **[ComfyUI guide](docs/COMFYUI_GUIDE.md)** -- How to replicate
  the pipeline with open-source tools
- **[Verification guide](docs/VERIFICATION_GUIDE.md)** -- How to
  independently verify these findings
- **[Marketing claims](evidence/marketing_claims.md)** -- 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](docs/METHODOLOGY.md).


## 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](docs/VERIFICATION_GUIDE.md).


## 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:

1. Marketing language that attributes open-source outputs to
   proprietary technology
2. Investor-facing claims about a "foundational model" that appears
   to be a pipeline of open-source components with proprietary weights
3. A CVPR paper describing a physics-based architecture that does
   not appear to match the deployed application
4. 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](https://creativecommons.org/licenses/by/4.0/).