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@@ -18,10 +18,22 @@ JavaScript module using TensorFlow/JS Machine Learning library
 
 <br>
 
+*Check out [**Simple Live Demo**](https://vladmandic.github.io/human/demo/typescript/index.html) fully annotated app as a good start starting point ([html](https://github.com/vladmandic/human/blob/main/demo/typescript/index.html))([code](https://github.com/vladmandic/human/blob/main/demo/typescript/index.ts))*  
+
+*Check out [**Main Live Demo**](https://vladmandic.github.io/human/demo/index.html) app for advanced processing of of webcam, video stream or images static images with all possible tunable options*  
+
+- To start video detection, simply press *Play*  
+- To process images, simply drag & drop in your Browser window  
+- Note: For optimal performance, select only models you'd like to use
+- Note: If you have modern GPU, WebGL (default) backend is preferred, otherwise select WASM backend
+
+<br>
+
 ## Demos
 
 - [**List of all Demo applications**](https://github.com/vladmandic/human/wiki/Demos)
 - [*Live:* **Main Application**](https://vladmandic.github.io/human/demo/index.html)
+- [*Live:* **Simple Application**](https://vladmandic.github.io/human/demo/typescript/index.html)
 - [*Live:* **Face Extraction, Description, Identification and Matching**](https://vladmandic.github.io/human/demo/facematch/index.html)
 - [*Live:* **Face Extraction and 3D Rendering**](https://vladmandic.github.io/human/demo/face3d/index.html)
 - [*Live:* **Multithreaded Detection Showcasing Maximum Performance**](https://vladmandic.github.io/human/demo/multithread/index.html)
@@ -45,6 +57,7 @@ JavaScript module using TensorFlow/JS Machine Learning library
 - [**Configuration Details**](https://github.com/vladmandic/human/wiki/Config)
 - [**Result Details**](https://github.com/vladmandic/human/wiki/Result)
 - [**Caching & Smoothing**](https://github.com/vladmandic/human/wiki/Caching)
+- [**Input Processing**](https://github.com/vladmandic/human/wiki/Image)
 - [**Face Recognition & Face Description**](https://github.com/vladmandic/human/wiki/Embedding)
 - [**Gesture Recognition**](https://github.com/vladmandic/human/wiki/Gesture)
 - [**Common Issues**](https://github.com/vladmandic/human/wiki/Issues)
@@ -72,8 +85,231 @@ JavaScript module using TensorFlow/JS Machine Learning library
 
 *Suggestions are welcome!*  
 
+<hr><br>
+
+## Examples
+
+Visit [Examples galery](https://vladmandic.github.io/human/samples/samples.html) for more examples  
+<https://vladmandic.github.io/human/samples/samples.html>
+
+![samples](assets/samples.jpg)
+
+<br>
+
+## Options
+
+All options as presented in the demo application...  
+> [demo/index.html](demo/index.html)  
+
+![Options visible in demo](assets/screenshot-menu.png)
+
+<br>
+
+**Results Browser:**  
+[ *Demo -> Display -> Show Results* ]<br>
+![Results](assets/screenshot-results.png)
+
+<br>
+
+## Advanced Examples
+
+1. **Face Similarity Matching:**  
+Extracts all faces from provided input images,  
+sorts them by similarity to selected face  
+and optionally matches detected face with database of known people to guess their names
+> [demo/facematch](demo/facematch/index.html)  
+
+![Face Matching](assets/screenshot-facematch.jpg)
+
+<br>
+
+2. **Face3D OpenGL Rendering:**  
+> [demo/face3d](demo/face3d/index.html)
+
+![Face Matching](assets/screenshot-face3d.jpg)
+
+<br>
+
+3. **VR Model Tracking:**  
+![vrmodel](assets/screenshot-vrm.jpg)
+
+<br>
+
+**468-Point Face Mesh Defails:**  
+(view in full resolution to see keypoints)  
+
+![FaceMesh](assets/facemesh.png)
+
+<br><hr><br>
+
+## Quick Start
+
+Simply load `Human` (*IIFE version*) directly from a cloud CDN in your HTML file:  
+(pick one: `jsdelirv`, `unpkg` or `cdnjs`)
+
+```html
+<script src="https://cdn.jsdelivr.net/npm/@vladmandic/human/dist/human.js"></script>
+<script src="https://unpkg.dev/@vladmandic/human/dist/human.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/human/2.1.5/human.js"></script>
+```
+
+For details, including how to use `Browser ESM` version or `NodeJS` version of `Human`, see [**Installation**](https://github.com/vladmandic/human/wiki/Install)
+
+<br>
+
+## Inputs
+
+`Human` library can process all known input types:  
+
+- `Image`, `ImageData`, `ImageBitmap`, `Canvas`, `OffscreenCanvas`, `Tensor`,  
+- `HTMLImageElement`, `HTMLCanvasElement`, `HTMLVideoElement`, `HTMLMediaElement`
+
+Additionally, `HTMLVideoElement`, `HTMLMediaElement` can be a standard `<video>` tag that links to:
+
+- WebCam on user's system
+- Any supported video type  
+  For example: `.mp4`, `.avi`, etc.
+- Additional video types supported via *HTML5 Media Source Extensions*  
+  Live streaming examples:
+  - **HLS** (*HTTP Live Streaming*) using `hls.js`
+  - **DASH** (Dynamic Adaptive Streaming over HTTP) using `dash.js`
+- **WebRTC** media track using built-in support  
+
+<br>
+
+## Example
+
+Example simple app that uses Human to process video input and  
+draw output on screen using internal draw helper functions
+
+```js
+// create instance of human with simple configuration using default values
+const config = { backend: 'webgl' };
+const human = new Human(config);
+// select input HTMLVideoElement and output HTMLCanvasElement from page
+const inputVideo = document.getElementById('video-id');
+const outputCanvas = document.getElementById('canvas-id');
+
+function detectVideo() {
+  // perform processing using default configuration
+  human.detect(inputVideo).then((result) => {
+    // result object will contain detected details
+    // as well as the processed canvas itself
+    // so lets first draw processed frame on canvas
+    human.draw.canvas(result.canvas, outputCanvas);
+    // then draw results on the same canvas
+    human.draw.face(outputCanvas, result.face);
+    human.draw.body(outputCanvas, result.body);
+    human.draw.hand(outputCanvas, result.hand);
+    human.draw.gesture(outputCanvas, result.gesture);
+    // and loop immediate to the next frame
+    requestAnimationFrame(detectVideo);
+  });
+}
+
+detectVideo();
+```
+
+or using `async/await`:
+
+```js
+// create instance of human with simple configuration using default values
+const config = { backend: 'webgl' };
+const human = new Human(config); // create instance of Human
+const inputVideo = document.getElementById('video-id');
+const outputCanvas = document.getElementById('canvas-id');
+
+async function detectVideo() {
+  const result = await human.detect(inputVideo); // run detection
+  human.draw.all(outputCanvas, result); // draw all results
+  requestAnimationFrame(detectVideo); // run loop
+}
+
+detectVideo(); // start loop
+```
+
+or using `Events`:
+
+```js
+// create instance of human with simple configuration using default values
+const config = { backend: 'webgl' };
+const human = new Human(config); // create instance of Human
+const inputVideo = document.getElementById('video-id');
+const outputCanvas = document.getElementById('canvas-id');
+
+human.events.addEventListener('detect', () => { // event gets triggered when detect is complete
+  human.draw.all(outputCanvas, human.result); // draw all results
+});
+
+function detectVideo() {
+  human.detect(inputVideo) // run detection
+  .then(() => requestAnimationFrame(detectVideo)); // upon detect complete start processing of the next frame
+}
+
+detectVideo(); // start loop
+```
+
+or using interpolated results for smooth video processing by separating detection and drawing loops:
+
+```js
+const human = new Human(); // create instance of Human
+const inputVideo = document.getElementById('video-id');
+const outputCanvas = document.getElementById('canvas-id');
+let result;
+
+async function detectVideo() {
+  result = await human.detect(inputVideo); // run detection
+  requestAnimationFrame(detectVideo); // run detect loop
+}
+
+async function drawVideo() {
+  if (result) { // check if result is available
+    const interpolated = human.next(result); // calculate next interpolated frame
+    human.draw.all(outputCanvas, interpolated); // draw the frame
+  }
+  requestAnimationFrame(drawVideo); // run draw loop
+}
+
+detectVideo(); // start detection loop
+drawVideo(); // start draw loop
+```
+
+And for even better results, you can run detection in a separate web worker thread
+
+<br><hr><br>
+
+## Default models
+
+Default models in Human library are:
+
+- **Face Detection**: MediaPipe BlazeFace Back variation
+- **Face Mesh**: MediaPipe FaceMesh
+- **Face Iris Analysis**: MediaPipe Iris
+- **Face Description**: HSE FaceRes
+- **Emotion Detection**: Oarriaga Emotion
+- **Body Analysis**: MoveNet Lightning variation
+- **Hand Analysis**: HandTrack & MediaPipe HandLandmarks
+- **Body Segmentation**: Google Selfie
+- **Object Detection**: CenterNet with MobileNet v3
+
+Note that alternative models are provided and can be enabled via configuration  
+For example, `PoseNet` model can be switched for `BlazePose`, `EfficientPose` or `MoveNet` model depending on the use case  
+
+For more info, see [**Configuration Details**](https://github.com/vladmandic/human/wiki/Configuration) and [**List of Models**](https://github.com/vladmandic/human/wiki/Models)
+
+<br><hr><br>
+
+## Diagnostics
+
+- [How to get diagnostic information or performance trace information](https://github.com/vladmandic/human/wiki/Diag)
+
 <br><hr><br>
 
 `Human` library is written in `TypeScript` [4.4](https://www.typescriptlang.org/docs/handbook/intro.html)  
-Conforming to `JavaScript` [ECMAScript version 2020](https://www.ecma-international.org/ecma-262/11.0/index.html) standard  
-Build target is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/9.0/)  
+Conforming to latest `JavaScript` [ECMAScript version 2021](https://262.ecma-international.org/) standard  
+Build target is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/11.0/)  
+
+<br>
+
+For details see [**Wiki Pages**](https://github.com/vladmandic/human/wiki)  
+and [**API Specification**](https://vladmandic.github.io/human/typedoc/classes/Human.html)