# MediaPipe Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/mediapipe
> Markdown URL: https://aitinkerers.org/technologies/mediapipe.md
> Technology record last updated: 2026-09-18T15:13:38Z
> Generated: 2026-09-21T19:48:15Z

Google's open-source, cross-platform framework for building and deploying real-time machine learning pipelines across mobile, web, and edge devices.

MediaPipe is a powerful, open-source framework from Google for applied machine learning (ML). It enables developers to construct complex, multimodal pipelines using a graph-based dataflow model: data moves through connected 'Calculators' for efficient, real-time processing of video, audio, and text . The core strength is its cross-platform capability: build once, and deploy seamlessly across Android, iOS, web (JavaScript), desktop, and IoT devices (like Coral) . MediaPipe offers ready-to-use 'Solutions' (Tasks) for critical computer vision applications, including Face Detection, Hand Tracking, and Pose Estimation (detecting 33 key body points) . This streamlined approach accelerates ML integration, minimizing the need for extensive model tuning or infrastructure setup .

- Official technology site: https://developers.google.com/mediapipe
- Public AI Tinkerers demos and talks: 7
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Building Emotional Intelligence for AI](https://cairo.aitinkerers.org/talks/rsvp_EHAXdxhFkPo)

we have built the first emotional intelligence engine for AI, to help AI, understand and interact with humans, we have trained ai with emotional intelligence to preform better interacting with humans, this is not just for AI, it's built for wellness, robotics, gaming, IOT

- Event context: AI Tinkerers Cairo: Inaugural Meetup — 2026-07-01 — Cairo
- Public talk page: https://cairo.aitinkerers.org/talks/rsvp_EHAXdxhFkPo

### [EvoFit: Building a Cross-Cultural AI Fitness Coach That Bridges Eastern Wellness and Western Exercise Science](https://hong-kong.aitinkerers.org/talks/rsvp_dvIFWxBl4TM)

EvoFit is an AI-powered fitness system that acts as a personalized coach — combining Eastern wellness traditions like Tai Chi with Western exercise science, all within a single app. Demo:For the demo: we're showcasing three live modules. The Movement Module lets users upload a workout video (e.g. squats) and receive AI-generated form feedback and scoring. The Real-Time Coaching Module uses your device camera for live Tai Chi practice — the system tracks your pose frame-by-frame, coaches you through movements, and scores your form in real time. The Food Module lets you photograph a meal to get instant calorie and nutrition analysis, or input your available ingredients and fitness goals to have AI generate a personalized recipe complete with instructions, macros, and who it suits best.

- Event context: AI Tinkerers Hong Kong at AWS: Agentic AI in Action (April) — 2026-04-29 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_dvIFWxBl4TM

### [Using AI to channel Olympic excitement into skill development of homegrown athletes](https://toronto.aitinkerers.org/talks/rsvp_pFLrl0ONOj0)

I coach at a speedskating club, and one of the most difficult aspects of training young athletes is focusing them on technical aspects, tracking progress, and communicating tips and form correction effectively. This is a mobile app designed specifically with speedskaters in mind, and it gamifies the process of holding a "basic position" i.e. the crouched-over skating position. The user begins a game and video is captured. A wireframe is overlaid on the athlete, and the knee and hip angles are calculated. When the athlete goes into basic position, the user enters a session and begins to accrue a score. Score accrual multipliers are applied the closer to "perfect form" an athlete has, which has visual indicators displayed in realtime. Feedback from an AI coach is also given in realtime, which keeps the user engaged and focused. The user finishes a session after they stand up, and a report generated by AI is shown to help them understand what could be improved, as well as their final score. AI also uses the user's home country to query a public API on speedskater results and world records (ex. a Canadian user would be shown world records and stats of famous skaters such as Laurent Dubreuil). It helps users connect more with how strong the Olympic-level athletes are, along with recording progress and showing improvement over time.

- Event context: AI Tinkerers Toronto - February 2026 @ Cohere! — 2026-02-26 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_pFLrl0ONOj0

### [Exercise Posture Tracking Using Computer Vision](https://raleigh.aitinkerers.org/talks/rsvp_QACg5zj28MM)

I've created an unsupervised computer vision algorithm to detect back rounding during deadlift. In short, I've demonstrated that I can measure the curvature of the back in an unsupervised way, and that a rounded back produces a different signal from a flat back. I've used off-the-shelf pose tracking and segmentation models to measure the back curvature. This could help guide learning of proper form during deadlift and other exercises.

- Event context: AI Tinkerers Raleigh Meetup — February 11, 2026 — 2026-02-11 — Raleigh
- Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_QACg5zj28MM

### [Super Coool Super Awesome Tennis Serve Analyzer](https://montreal.aitinkerers.org/talks/rsvp_4lDuqbxrR9s)

I love playing tennis, my serve is wildly inconsitent and cost me a lot of games. Instructors are pricey and I'm cheap and don't want to shell out 100$ an hour for someone to feeed me a handle full of tips that I could have googled myself. logically to improve ones serve on might actually just go and practice but that would be far to simple. instead I thought the best way to improve my tennis serve would be to sit behind a keyboard and build an web-app powered by AI to tell me how I'm messing up my serve. It got cold and snow came so I wasn't able to actually use it :( but In the presentation I will walk through the implementaiton details and give a demo

- Event context: AI Tinkerers Montreal: Demo Night — November 20, 2025 — 2025-11-20 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_4lDuqbxrR9s

### [Virtual avatar generation models as world navigators](https://sf.aitinkerers.org/talks/rsvp_VKgGo_Qx060)

A novel video model capable of simulating human movement within a given environment by assuming the parameters of a virtual avatar.

- Event context: AI Tinkerers - San Francisco - Summer Edition - July 2024 — 2024-07-12 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_VKgGo_Qx060

### [Face Morphing through DL and Convex Combinations](https://medellin.aitinkerers.org/talks/rsvp_GW1J70Ocieo)

Demostraré un pipeline para hacer morphing entre dos imágenes de entrada que contienen rostos.. Este pipeline consta de dos pasos: 1. Detección de rostros y de puntos de referencia en rostros usando dos modelos de Deep learning disponibles libremente. 2. Generación de una nueva imagen como una interpolación convexa entre las dos imágenes de entrada, informalmente "alpha * Img1 + (1-alpha) * Img2". Cada pixel de esta imagen se calcula a partir de una combinación convexa de pixeles correspondientes las imagenes de entrada.

- Event context: Lanzamiento de AI Tinkerers #1 — 2024-04-25 — Medellín
- Public talk page: https://medellin.aitinkerers.org/talks/rsvp_GW1J70Ocieo

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