Couple Face Swap AI Photo
Fun couple AI face swap app for social media sharing
Agency
Nep Tech Pal Pvt. Ltd.
Category
android
Type
android
Status
published
Key Features
Discover the powerful features that make this project stand out.
Couple Face Swap
Specialized dual face-swap engine optimized for two-person couple photos.
iOS & Android
Available on both Google Play Store and Apple App Store.
Video Face Swap
Swap faces in short video clips for Instagram Reels and TikTok content.
Fun Filters
Apply couple-themed overlays and stickers after face swapping.
Privacy First
All photos are processed and deleted immediately — never stored on servers.
Share in 1 Tap
Direct sharing to Instagram, TikTok, WhatsApp and other platforms.
From Challenge to Solution
Discover how we transformed challenges into innovative solutions.
The Challenge
The Challenge: Two-Person Face Swaps Are Significantly More Complex
Swapping one face in a photo is hard. Swapping two faces simultaneously in a couple photo is qualitatively harder — not just twice the work, but an entirely different set of failure modes.
The specific challenges unique to two-face scenarios:
- Face ordering ambiguity: When swapping two faces, Face A's identity goes to Face B's position and vice versa. But which face is 'A' and which is 'B'? The model must deterministically identify and label faces to avoid swapping a person's face with their own, or getting the swap direction reversed (which produces a confusing, not funny result).
- Boundary artifacts at adjacent faces: In close-together couple photos, the face regions overlap or are separated by only a few pixels. The blending mask for one face must not bleed into the adjacent face's region — requiring precise segmentation masks rather than rectangular bounding boxes.
- Inconsistent lighting between two faces: In most couple photos, each person is lit slightly differently (different angles to the light source). Each face swap must apply independent color harmonization, not a single global correction.
- Video face swap complexity: Frame-by-frame swapping requires face tracking — the same face must be identified and swapped consistently across frames even as the person moves. A tracking failure mid-video produces jarring identity flips that make the result useless.
- Privacy sensitivity: Couple photos are intimate. Users needed to trust that their personal photos would not be stored, shared, or used for AI training. Privacy features had to be technically real, not just marketing language.
Our Solution
Solution: Two-Pass Detection with Independent Swap and Gradient Blending
We designed the pipeline specifically around the two-face use case — every step was engineered with the challenges of couple photos in mind.
1. Two-Pass Face Detection with Ordering Logic
RetinaFace detects all faces with confidence scores and bounding boxes. A ordering algorithm assigns consistent left-to-right labels to detected faces, ensuring deterministic swap direction. For photos where faces are close together, instance segmentation masks (rather than bounding boxes) precisely delimit each face's swap region. This eliminates the boundary bleed problem that causes artifacts on adjacent faces.
2. Independent Per-Face Swap and Harmonization
Each face is swapped independently — Face A's swap pipeline runs first, then Face B's — with the output of A's swap used as input for B's. Each face gets its own independent color harmonization step calibrated to that face's lighting environment. Gradient blending (Poisson image editing) at each face boundary creates seamless integration with the surrounding skin and hair. The result is two photorealistic swaps in a single image with no visual artifacts at either boundary.
3. Video Face Tracking with MediaPipe
For video swap, MediaPipe's face tracking maintains persistent face identity across frames — each tracked face retains its assigned identity label throughout the video, preventing mid-video identity flips. The pipeline processes video at 10fps (a practical quality/speed tradeoff for short social media clips) and interpolates smoothly between processed frames for a fluid result. Output videos are compressed to under 15MB for fast sharing — matching the file size constraints of Instagram Reels and TikTok upload requirements.
4. Technically Enforced Privacy
Photos are uploaded to a temporary S3 path that is automatically deleted by a Lambda trigger 60 seconds after processing completes. The inference pipeline processes images in memory only — no persistent disk writes. User IDs are randomized per-session (no account required for basic usage). Privacy isn't a checkbox here — it's architecturally impossible to retrieve a user's photo after 60 seconds, because the storage path no longer exists.
Technology Stack
The powerful technologies used to bring this project to life.
FastAPI
Backend
InsightFace
Backend
Python
Backend
Firebase
DevOps
Dart
Mobile
Flutter
Mobile