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WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

CODE: https://github.com/Ascend-Research/HeadPoseEstimation-WHENet

This channels is for Programmers, Coders, Software Engineers.

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⚡️ 500 AI Machine learning Deep learning Computer vision NLP Projects with code

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InstantID : Zero-shot Identity-Preserving Generation in Seconds

Free Source Code: https://github.com/InstantID/InstantID.

MLOps Masterclass

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Tracking Any Point (TAP)

Welcome to the official Google Deepmind repository for Tracking Any Point (TAP), home of the TAP-Vid Dataset, our top-performing TAPIR model, and our RoboTAP extension.

Source code: https://github.com/google-deepmind/tapnet

Google Colab: https://github.com/google-deepmind/tapnet

join us: @MachineLearning_Programming

This channels is for Programmers, Coders, Software Engineers.

0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
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7- Deep Learning
8- programming Languages

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EfficientViT - SAM:69x Faster SAM: Multi-Scale Linear Attention for High-Resolution Dense Prediction

1. Channel: @deeplearning_ai

2.Source Code: https://github.com/mit-han-lab/efficientvit

3. Paper: https://arxiv.org/abs/2402.05008

🌴🌴Direct-a-Video: driving Video Generation🌴🌴

👉Direct-a-Video is a text-to-video generation framework that allows users to individually or jointly control the camera movement and/or object motion. Authors: City University of HK, Kuaishou Tech & Tianjin.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
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👉Channel: @MachineLearning_Programming
👉Paper https://arxiv.org/pdf/2402.03162.pdf
👉Project https://direct-a-video.github.io/

🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!

Features:
* State-of-the-art accuracy
* Comprehensive tools for training, testing, and inference
* Easy setup with conda
* Supports multiple datasets

Watch the performance showcase on GitHub for future advancements.

[Source Code] [Paper]

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👉 @deeplearning_ai

📢 FREE TRAINING:
Navigating the Landscape of MLOps & LLMOps 🚀

🔥 Join our FREE MLOps course demo and acquire essential skills for AI and data science across Multicloud 🚀

👉 Reserve your seat now:
https://bit.ly/mlops-webinar

🌟 What you'll gain:
1️⃣ ML model deployment techniques on AWS, Azure, GCP & open source.
2️⃣ Efficient data management insights.
3️⃣ Explore the latest MLOps tools.
4️⃣ Real-time interaction with expert instructors.

🚩 Limited spots available! Don't miss out!

👉 Enroll now:
https://bit.ly/mlops-webinar

👥 Share with fellow ML enthusiasts! 🚀✨

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AiOS: The Future of Human Shape & Pose Recovery

Discover AiOS, the cutting-edge, unified framework by SenseTime, HKU, IDEA, S-Lab, and Shanghai AI Lab. AiOS redefines state-of-the-art expressive pose and shape recovery, seamlessly integrating advanced features without the need for separate human detection steps.

Highlights:

✅First-of-its-Kind: Single-stage EHPS with zero extra detection networks.
✅Innovative Design: Unique "Human-as-Tokens" concept for deeper insights.
✅Enhanced Dynamics: Sophisticated attention to human relationships.
✅Comprehensive Analysis: Unified feature system for unparalleled whole-body understanding.
✅Unmatched Performance: Top-tier results sans ground truth bounding boxes.

Explore More:
Project Page
Read the Paper

@MachineLearning_Programming