DeepLabCut · C-Achard · Mar 9, 2026 · Feb 6, 2026 · Feb 6, 2026 · Feb 6, 2026
diff --git a/_toc.yml b/_toc.yml
@@ -86,7 +86,21 @@ parts:
 
 - caption: DeepLabCut-Live!
   chapters:
-  - file: docs/deeplabcutlive
+  - file: docs/dlc-live/deeplabcutlive
+  - file: docs/dlc-live/dlc-live-gui/index
+    sections:
+      - file: docs/dlc-live/dlc-live-gui/quickstart/install
+      - file: docs/dlc-live/dlc-live-gui/user_guide/overview
+      - file: docs/dlc-live/dlc-live-gui/user_guide/cameras_backends/camera_support
+        sections:
+          - file: docs/dlc-live/dlc-live-gui/user_guide/cameras_backends/opencv_backend
+          - file: docs/dlc-live/dlc-live-gui/user_guide/cameras_backends/basler_backend
+          - file: docs/dlc-live/dlc-live-gui/user_guide/cameras_backends/aravis_backend
+          - file: docs/dlc-live/dlc-live-gui/user_guide/cameras_backends/gentl_backend
+      - file: docs/dlc-live/dlc-live-gui/user_guide/misc/misc_landing
+        sections:
+          - file: docs/dlc-live/dlc-live-gui/user_guide/misc/modelzoo_downloads
+          - file: docs/dlc-live/dlc-live-gui/user_guide/misc/timestamp_format
 
 - caption: "🦄 DeepLabCut Model Zoo"
   chapters:
@@ -107,4 +121,4 @@ parts:
 
 - caption: Citations for DeepLabCut
   chapters:
-  - file: docs/citation
+    - file: docs/citation
diff --git a/docs/ModelZoo.md b/docs/ModelZoo.md
@@ -1,4 +1,5 @@
-# The DeepLabCut Model Zoo! 
+(file:model-zoo)=
+# The DeepLabCut Model Zoo!
 
 ![image](https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/8957c690-4f27-4430-8581-4161fd58d052/68747470733a2f2f696d616765732e73717561726573706163652d63646e2e636f6d2f636f6e74656e742f76312f3537663664353163396637343536366635356563663237312f313631363439323337333730302d50474f41433732494f4236415545343756544a582f6b6531375a77644742546f646449.png?format=450w)
 
@@ -11,7 +12,7 @@ first proper [SuperAnimal Foundation Models](#about-the-superanimal-models) publ
 
 - (1) a collection of models that are trained on diverse data across (typically) large datasets, which means you do not need to train models yourself, rather you can use them in your research applications.
 - (2) a contribution website for community crowd sourcing of expertly labeled keypoints to improve models! You can get involved here: [contrib.deeplabcut.org](https://contrib.deeplabcut.org/).
-- (3) a no-install DeepLabCut that you can use on ♾[Google Colab](https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/COLAB/COLAB_DEMO_SuperAnimal.ipynb), 
+- (3) a no-install DeepLabCut that you can use on ♾[Google Colab](https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/COLAB/COLAB_DEMO_SuperAnimal.ipynb),
 test our models in 🕸[the browser](https://contrib.deeplabcut.org/), or on our 🤗[HuggingFace](https://huggingface.co/spaces/DeepLabCut/DeepLabCutModelZoo-SuperAnimals) app!
 - (4) new methods to make SuperAnimal Foundation Models that combine data across different labs/datasets, keypoints, animals/species, and use on your data!
 
@@ -22,7 +23,7 @@ pip install deeplabcut[gui,modelzoo]
 
 ## About the SuperAnimal Models
 
-Animal pose estimation is critical in applications ranging from neuroscience to veterinary medicine. However, reliable inference of animal poses currently requires domain knowledge and labeling effort. To ease access to high-performance animal pose estimation models across diverse environments and species, we present a new paradigm for pre-training and fine-tuning that provides excellent zero-shot (no training required) performance on two major classes of animal pose data: quadrupeds and lab mice. 
+Animal pose estimation is critical in applications ranging from neuroscience to veterinary medicine. However, reliable inference of animal poses currently requires domain knowledge and labeling effort. To ease access to high-performance animal pose estimation models across diverse environments and species, we present a new paradigm for pre-training and fine-tuning that provides excellent zero-shot (no training required) performance on two major classes of animal pose data: quadrupeds and lab mice.
 
 To provide the community with easy access to such high performance models across diverse environments and species, we present a new paradigm for building pre-trained animal pose models -- which we call SuperAnimal models -- and the ability to use them for transfer learning (e.g., fine-tune them if needed).
 
@@ -31,7 +32,7 @@ To provide the community with easy access to such high performance models across
 
 
 
-### SuperAnimal-Quadruped: 
+### SuperAnimal-Quadruped:
 
 
 - `superanimal_quadruped_x` models aim to work across a large range of quadruped animals, from horses, dogs, sheep, rodents, to elephants. The camera perspective is orthogonal to the animal ("side view"), and most of the data includes the animals face (thus the front and side of the animal). You will note we have several variants that differ in speed vs. performance, so please do test them out on your data to see which is best suited for your application. Also note we have a "video adaptation" feature, which lets you adapt your data to the model in a self-supervised way. No labeling needed!
@@ -63,7 +64,7 @@ To provide the community with easy access to such high performance models across
         - `superanimal_topviewmouse_dlcrnet` is a bottom-up model that predicts all keypoints then groups them into individuals. This can be faster, but more error prone.
     - `superanimal_topviewmouse` -> This is the same as `superanimal_topviewmouse_dlcrnet`, this was the old naming and being depreciated.
     - For all models, they are automatically downloaded to modelzoo/checkpoints when used.
-    
+
 -  Here are example images of what the model is trained on:
 ![SA-TVM](https://user-images.githubusercontent.com/28102185/209957260-c0db72e0-4fdf-434c-8579-34bc5f27f907.png)
 
@@ -76,9 +77,9 @@ To provide the community with easy access to such high performance models across
 
 ### Practical example: Using SuperAnimal models for inference without training.
 
-You can simply call the model and run video inference. 
+You can simply call the model and run video inference.
 
-To note, a good step is typically to use our self-supervised video adaptation method to reduce jitter. In the `deeplabcut.video_inference_superanimal` simply function set the `video_adapt` option to __True__. Be aware, that enabling this option will (minimally) extend the processing time. 
+To note, a good step is typically to use our self-supervised video adaptation method to reduce jitter. In the `deeplabcut.video_inference_superanimal` simply function set the `video_adapt` option to __True__. Be aware, that enabling this option will (minimally) extend the processing time.
 
 ```python
 import deeplabcut
@@ -114,7 +115,7 @@ deeplabcut.video_inference_superanimal([video_path],
 ```
 
 ### Practical example: Using transfer learning with superanimal weights.
-In the `deeplabcut.train_network` function, the `superanimal_transfer_learning` option plays a pivotal role. If it's set to __True__, it uses a new decoding layer and allows you to use superanimal weights in any project, no matter the number of keypoints. However, if it's set to __False__, you are doing fine-tuning. So, make sure your dataset has the right number of keypoints.  
+In the `deeplabcut.train_network` function, the `superanimal_transfer_learning` option plays a pivotal role. If it's set to __True__, it uses a new decoding layer and allows you to use superanimal weights in any project, no matter the number of keypoints. However, if it's set to __False__, you are doing fine-tuning. So, make sure your dataset has the right number of keypoints.
 
 Specifically:
     * `superanimal_quadruped_x` uses 39 keypoints

diff --git a/docs/deeplabcutlive.md b/docs/deeplabcutlive.md
diff --git a/docs/dlc-live/deeplabcutlive.md b/docs/dlc-live/deeplabcutlive.md
@@ -0,0 +1,18 @@
+(deeplabcut-live)=
+# Running DeepLabCut models in real-time
+
+We provide two additional packages that allow you to record and stream camera data and run DeepLabCut models in real-time.
+
+You can get an in-depth overview of this work in [Kane et al, 2020 eLife](https://elifesciences.org/articles/61909).
+
+## DLC-Live! Software SDK
+
+You may find more information on the DLC-Live! software SDK on GitHub:
+
+- [DLC-Live! Software](https://github.com/DeepLabCut/DeepLabCut-live)
+
+## DLC-Live! GUI
+
+For the DLC-Live! Camera GUI, we provide documentation in the section below:
+
+- {doc}`DLC-Live! GUI <./dlc-live-gui/index>`
diff --git a/docs/dlc-live/dlc-live-gui/_static/images/main_window_100226.png b/docs/dlc-live/dlc-live-gui/_static/images/main_window_100226.png
diff --git a/docs/dlc-live/dlc-live-gui/index.md b/docs/dlc-live/dlc-live-gui/index.md
@@ -0,0 +1,75 @@
+# DeepLabCut-live-GUI
+
+A graphical application for **real-time pose estimation with DeepLabCut** using one or more cameras.
+
+This GUI is designed for **scientists and experimenters** who want to preview, run inference, and record synchronized video with pose overlays—without writing code.
+
+## Table of Contents
+
+- {doc}`Installation <./quickstart/install>`
+- {doc}`Overview <./user_guide/overview>`
+- {doc}`Camera setup and backends <./user_guide/cameras_backends/camera_support>`
+- {doc}`Timestamp format and synchronization <./user_guide/misc/timestamp_format>`
+
+```{caution}
+Please be aware of the {ref}`sec:dlclivegui-index-limitations`
+```
+
+---
+
+## Description
+
+### What this software does
+
+- **Live camera preview** from one or multiple cameras
+- **Real-time pose inference** using DeepLabCut Live models
+- **Multi-camera support** with tiled display
+- **Video recording** (raw or with pose and bounding-box overlays)
+- **Session-based data organization** with reproducible naming
+- **Optional processor plugins** to extend behavior (e.g. remote control, triggers)
+
+The application is built with **PySide6 (Qt)** and is intended for interactive experimental use rather than offline batch processing.
+<!-- TODO mention and link to DLC live standalone -->
+
+### Typical workflow
+
+1. **Install** the application and required camera backends
+2. **Configure cameras** (single or multi-camera)
+3. **Select a DeepLabCut Live model**
+4. **Start preview** and verify frame rate
+5. **Run pose inference** on a selected camera
+6. **Record video** (optionally with overlays)
+   - With **organized results** by session and run
+
+Each of these steps is covered in the *{doc}`Quickstart <quickstart/install>`*
+and *{doc}`User Guide <user_guide/overview>`* sections of this documentation.
+
+### Who this is for
+
+- Neuroscience and behavior labs
+- Experimentalists running real-time tracking
+- Anyone who wants a **GUI-first** workflow for DeepLabCut Live
+
+---
+(sec:dlclivegui-index-limitations)=
+## Current limitations
+
+Before getting started, be aware of the following constraints:
+
+- Pose inference runs on **one selected camera at a time** (even in multi-camera mode)
+- Camera synchronization depends on **backend capabilities and hardware**
+  - OpenCV controls for resolution and FPS are "best effort" and may not work with all cameras.
+    Expect inconsistencies when setting certain frame rates or resolutions as resolution depends on the device driver.
+- DeepLabCut Live models must be **exported and compatible** with the selected backend
+  - Some SuperAnimal models from {ref}`file:model-zoo` may not work out of the box.<br>This is currently a known issue for:
+    - SuperHuman model (missing detector)
+- **Performance** depends on camera resolution, frame rate, GPU availability, and codec choice
+  - Expect bottlenecks with heavy models, multiple high-resolution cameras, or CPU-only inference.
+
+---
+
+## Feedback, issues, and contributions
+
+> *This project is under active development. Feedback from real experimental use is highly valued.*
+> 
+> [Please report issues, suggest features, or contribute to the codebase on GitHub !](https://github.com/DeepLabCut/DeepLabCut-live-GUI)