Background Remover Online – Free AI Cutout in Browser

How to Use Background Remover

Step 1: Upload a portrait, selfie, or person photo (JPG, PNG, WEBP).
Step 2: The in-browser AI model automatically segments the foreground subject.
Step 3: Choose Transparent background, Solid White, or pick a custom background color.
Step 4: Download your clean cutout as a high-resolution PNG or JPG.

One Resizer's Background Remover provides specialized browser-local controls tailored to its workflow:

AI Selfie Segmentation: Local WebAssembly model identifies human subjects and separates background pixels.
Background Modes: Choose Transparent (PNG with alpha channel), Crisp White (for official ID/passports), or Custom Hex Color.
Interactive Checkerboard Preview: Inspect cutout boundaries clearly against a high-contrast transparency grid.

Specification	Technical Capability
Accepted Input Formats	JPG, JPEG, PNG, WEBP
Output File Formats	PNG (Transparent), JPG (Solid color)
Processing Engine	MediaPipe Selfie Segmentation (WebAssembly & WebGL)
Network Privacy	Client-side processing (model weights load into browser cache)

Use photos with good contrast between the person and background for crisp hair and edge definition.
For passport and visa photos, select the Solid White background preset.
Save cutouts as PNG to preserve transparent backgrounds for design compositing.

Edge boundaries look rough on complex backgrounds: Ensure adequate lighting on the subject; high-contrast source photos produce the sharpest segmentation masks.
Model loading takes a moment: On first use, the browser downloads the WebAssembly AI model into cache; subsequent runs process locally in memory.

It utilizes an in-browser machine learning model (MediaPipe Selfie Segmentation) running via WebAssembly and WebGL directly on your device GPU/CPU.

Yes, you can select transparent background, crisp white, or pick any custom solid background color.

It is optimized for portraits, headshots, selfie photos, ID cards, and people with clear subject-to-background contrast.

The machine learning model weights load into browser cache upon initial tool launch; once loaded, segmentation runs locally in memory.