AI Depth Map Generator
Upload any photo and generate a stunning depth map visualization using AI. See how far away each part of the scene is from the camera. Great for 3D effects, AR/VR, and creative photography. Runs 100% in your browser.
Drop a photo here or click to upload
Supports JPG, PNG, WebP
How AI Depth Estimation Works
This tool uses Depth Anything, a foundation model for monocular depth estimation developed by researchers at TikTok and the University of Hong Kong. It predicts how far each pixel in a photo is from the camera, creating a depth map from a single 2D image. The model processes visual cues like perspective, texture gradients, object sizes, and occlusion patterns to infer 3D structure.
The AI model runs entirely in your browser using Transformers.js. It is downloaded once (around 50 MB) and cached for offline use. No image data ever leaves your device.
Reading a Depth Map
- Red/warm colors — Objects close to the camera (foreground)
- Blue/cool colors — Objects far from the camera (background)
- Green/yellow — Mid-range distances
- Smooth gradients — Continuous surfaces like floors, walls
- Sharp edges — Distinct depth boundaries between objects
Creative and Practical Uses
Depth maps unlock a world of creative possibilities. Use them to create parallax 3D effects from flat photos, generate bokeh (background blur) in post-processing, or build AR experiences that interact with scene geometry. In 3D modeling, depth maps help create displacement maps for realistic terrain and surfaces.
Photographers use depth maps to understand scene composition and depth of field. Game developers use them for collision detection and scene reconstruction. Robotics engineers use depth estimation for navigation and obstacle avoidance. This tool lets you experiment with all these concepts for free.
Tips for Better Depth Maps
Photos with clear foreground and background separation produce the most dramatic depth maps. Outdoor landscapes, room interiors, and street scenes work exceptionally well. Avoid flat, textureless images (like a white wall) as there are fewer depth cues for the model to work with. Higher resolution images give more detailed depth maps.