Cling — AI Video Background Remover
AI-Based Android Application Development Case Study
About the Project
Cling is an AI-powered Android application designed to automatically remove backgrounds from photos and videos directly on the user's device. This application was developed to help content creators, online sellers, and general users create professional visuals without needing complex editing software.
This project focuses on a fast, simple, and privacy-preserving user experience because the entire process is performed on-device without uploading files to external servers.
Background
Many current background remover apps require users to upload their files to the cloud before processing. Besides slowing down the workflow, this approach also raises privacy concerns.
With Cling, I wanted to deliver a solution that is:
- Fast and lightweight
- Capable of processing both photos and videos
- Built with user privacy in mind
- Easy to use, even for non-technical users
Key Features
✨ AI Background Removal
Automatically remove backgrounds from photos and videos with a single tap using AI segmentation technology.
🎥 Video Background Remover
Users can automatically remove video backgrounds for social media content, product promotions, and creative editing needs.
🖼️ Photo Background Eraser
Separate objects, people, or products from backgrounds with high-quality transparent results.
👤 Selfie Mode & General Mode
The application provides two processing modes:
- Selfie Mode: Optimized for portraits and people.
- General Mode: Optimized for objects and complex scenes.
🔒 Privacy First
The entire process is carried out directly on the device (on-device processing), ensuring users' files are never sent to external servers.
📚 History & Comparison
Users can view their editing history and compare before/after results side-by-side in real time.
Technologies Used
| Technology | Function |
|---|---|
| Flutter | Cross-platform mobile development |
| AI Segmentation Model | Background removal engine |
| Android SDK | Native Android integration |
| Media Processing | Video and image processing |
| On-device AI Processing | Privacy and performance optimization |
Key Challenges
1. Video Processing Optimization
Processing background removal on videos requires high performance and memory optimization to run smoothly across various Android devices.
2. Maintaining Cutout Quality
Balancing processing speed and cutout quality was one of the primary technical challenges.
3. User Experience
I strived to keep the editing workflow simple and intuitive, despite the underlying AI features being relatively complex.
Project Results
Key achievements of this project include:
- The application successfully runs on various Android devices.
- Seamless background removal support for both photos and videos.
- Processing is performed entirely without uploading files to the cloud.
- The UI is designed to be simple, clean, and intuitive.
Key Takeaways & Learnings
Through this project, I gained valuable experience in:
- Implementing AI models in mobile applications.
- Optimizing performance for media processing.
- Managing memory on Android devices.
- Designing UX for editing applications.
- Deploying and distributing apps via the Google Play Store.
Project Links
- Google Play Store: https://play.google.com/store/apps/details?id=com.yotriv.cling
Conclusion
Cling is a key project that deepened my expertise in AI-driven Android application development. In addition to technical development, this project taught me the importance of building fast, simple, and privacy-respecting user experiences.
The project will continue to be developed with new features and improvements to the AI processing quality in future versions.