✨ Code2Video | Python
🎯 Primary Use Case:
Generating educational videos based on code execution and logic.
✨ Key Features:
• Video generation from code
• Educational video creation
• Code-centric paradigm
• Multi-agent system utilization (implied)
📖 Summary:
Code2Video is a project focused on generating educational videos from code. It presents a code-centric paradigm for creating teaching videos, potentially leveraging multi-agent systems to automate the video creation process. The project provides resources such as a paper, dataset, and project website for further exploration.
🔗 Links:
• View Project
• Homepage
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🔓 Open Source
🎯 Primary Use Case:
Generating educational videos based on code execution and logic.
✨ Key Features:
• Video generation from code
• Educational video creation
• Code-centric paradigm
• Multi-agent system utilization (implied)
📖 Summary:
Code2Video is a project focused on generating educational videos from code. It presents a code-centric paradigm for creating teaching videos, potentially leveraging multi-agent systems to automate the video creation process. The project provides resources such as a paper, dataset, and project website for further exploration.
🔗 Links:
• View Project
• Homepage
================
🔓 Open Source
🔥 doomscroll-detector | Python
🎯 Primary Use Case:
Detecting and discouraging doomscrolling behavior.
✨ Key Features:
• YOLOv11 pose estimation for keypoint tracking
• YOLO object detection for phone detection
• Heuristics for detecting reclined posture based on hip/shoulder alignment
• Heuristics for detecting phone holding based on wrist proximity
• Overlay of results on webcam feed with a spoofed penalty counter
📖 Summary:
The Doomscrolling Detector is a computer vision pipeline that identifies when a user is reclined and using their phone, indicative of doomscrolling behavior. It uses pose estimation and object detection to flag these instances, and includes a spoofed penalty counter as a humorous deterrent.
🔗 Links:
• View Project
================
🔓 Open Source
🎯 Primary Use Case:
Detecting and discouraging doomscrolling behavior.
✨ Key Features:
• YOLOv11 pose estimation for keypoint tracking
• YOLO object detection for phone detection
• Heuristics for detecting reclined posture based on hip/shoulder alignment
• Heuristics for detecting phone holding based on wrist proximity
• Overlay of results on webcam feed with a spoofed penalty counter
📖 Summary:
The Doomscrolling Detector is a computer vision pipeline that identifies when a user is reclined and using their phone, indicative of doomscrolling behavior. It uses pose estimation and object detection to flag these instances, and includes a spoofed penalty counter as a humorous deterrent.
🔗 Links:
• View Project
================
🔓 Open Source