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🌴🌴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:
Decoupling camera/object motion in gen-AI
Allowing users to independently/jointly control
Novel temporal cross-attention for cam motion
Training-free spatial cross-attention for objects
Driving object generation via bounding boxes

hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse

👉Channel: @MachineLearning_Programming
👉Paper https://arxiv.org/pdf/2402.03162.pdf
👉Project https://direct-a-video.github.io/
LeGrad: Layerwise Explainability GRADient method for large ViT transformer architectures

Explore More:
💻DEMO:
you may use demo
📖Read the Paper:
Access Here
💻Source Code: Explore on GitHub

Relevance:
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join our community:
👉
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🚀 Discover LiteHPE: Advanced Head Pose Estimation 🚀

Features:
🛠️ Setup in Minutes:
📈 Top-Tier Performance:
Achieve low Mean Absolute Error rates
Models range from MobileOne_s0 to s4
Pretrained models ready for download

🌟 🌟 Star us on GitHub for the latest updates: LiteHPE on GitHub.

Boost your project's capabilities with LiteHPE – the forefront of head pose estimation technology!

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🔗 Join now:
@MachineLearning_Programming
🚀 MLOps Market to reach US$4 Billion in 2025

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Navigating the Landscape of MLOps & LLMOps

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🚀 Share with ML enthusiasts! #MLOps #AI #TechTraining
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🚀 3DGazeNet: Revolutionizing Gaze Estimation with Weak-Supervision! 🌟

Key Features:
🔹 Advanced Neural Network:
Built on the robust U2-Net architecture.
🔹 Comprehensive Utilities:
Easy data loading, preprocessing, and augmentation.
🔹 Seamless Integration:
Train, test, and visualize with simple commands.

Demo Visualization:Visualize the demo by configuring your video path in main.py and showcasing the power of 3DGazeNet.

Pretrained Weights:Quick start with our pretrained weights stored in the weights folder.

💻Source Code: https://github.com/Shohruh72/3DGazeNet
📖Read the Paper: Access Here


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🚀 The Future of Object Detection is Here!

🔍Achieve state-of-the-art results with just one line of code! 🔥


🎥 Live Demos & Code: GitHub Repo

📥 Pretrained Models: Ready for download—plug and play!

⭐️ Support innovation! Star the repo now 👉 GitHub Link

📢 Join our ML community: @MachineLearning_Programming

#MachineLearning #AI #ObjectDetection #YOLO #OpenSource #DevCommunity #TechInnovation
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🚀 PIPNet: One-Click Facial Landmark Detection 🎯

🔗 GitHub: Star the Repo | 🎥 Watch Dem

🔥 Key Features:
One-Click Inference – Just run & detect!
High Accuracy (300W dataset) 📊
ResNet-powered for robustness
Supports training, testing & real-time demo 🎥

📌 Run Inference in One Click:
python main.py --demo

📊 Performance:
🔹 ResNet101 (120 epochs) → 3.17 NME

🌟 Support Open Source! Star & Share!

🔗 GitHub Repo

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🧠 VGGT: Visual Geometry Grounded Transformer

⚡️ Fast, accurate 3D scene understanding from just a few images — no post-processing needed!

🎯 Outputs: camera pose, depth, point cloud & 3D tracking
🔥 Boosts tasks like view synthesis & non-rigid tracking

📄 Read the paper
💻 Code on GitHub

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@MachineLearning_Programming
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