ATH-MaaS/Pixelle-Video

🚀 AI 全自动短视频引擎 | AI Fully Automated Short Video Engine

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Summary Information

Updated 18 minutes ago
Added to GitGenius on May 3rd, 2026
Created on November 7th, 2025
Open Issues & Pull Requests: 156 (+0)
Number of forks: 3,869
Total Stargazers: 26,772 (+0)
Total Subscribers: 105 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 23.0 hours
Mean response time: 4.2 days
90th percentile: 11.7 days
Tracked items: 115

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 47% of tracked open issues have had no activity in three months. Only 7% of issues opened in the past year have been closed. Three people close 69% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 137
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 77 days
Stale 30+ days: 126
Stale 90+ days: 65

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 1
Events in 7 days: 1

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Detailed Description

Pixelle-Video is an AI-powered video generation engine that automates the creation of short videos from a single topic input.

The tool solves the problem of time-consuming video production by automating the entire workflow. Given only a topic, it generates video scripts, creates AI-generated images or video content, synthesizes voiceover narration, adds background music, and assembles the final video in one step. The system requires no editing experience and handles the technical complexity internally through integration with multiple AI models and services.

The tool suits creators, content producers, and marketers who need to generate videos quickly without specialized skills or equipment. It works well for projects requiring rapid content production at scale, such as social media content, educational videos, or automated content pipelines. The project provides a web interface for ease of use and supports various configurations for image and video model providers, allowing users to choose their preferred AI backends.

The project maintains active engagement with its user base, with almost all open issues coming from external users rather than the core team, demonstrating substantial real-world adoption. Maintainers typically respond to new issues and pull requests within a day, indicating responsive and consistent development activity.