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Android Face Fusion

Android app for face swapping using ONNX models on-device. Port of the Python FaceFusion pipeline.

Quick Start

  1. Open in Android Studio, sync Gradle, build & run
  2. On first launch, models auto-download from HuggingFace (~739 MB)
  3. Select source image (face to use) and target image (face to replace)
  4. Tap Swap Faces

Models

All models download automatically on first launch from huggingface.co/leonelhs/insightface. Manual placement in app/src/main/assets/ also works.

Model Purpose Input Output Size
det_10g.onnx Face detection (SCRFD) 640x640 RGB Boxes + 5 landmarks ~16 MB
w600k_r50.onnx Face embedding (ArcFace) 112x112 aligned face 512-dim vector ~166 MB
inswapper_128.onnx Face swapping (INSwapper) 128x128 face + 512-dim embedding 128x128 swapped face ~553 MB

EMAP

The INSwapper model requires a 512x512 transformation matrix (EMAP) extracted from its last graph initializer. Pre-extracted as app/src/main/assets/emap.bin. To re-extract:

python extract_emap.py
cp emap.bin app/src/main/assets/

Pipeline

  1. Detect faces in both images (SCRFD, 640x640, norm: (px-127.5)/128)
  2. Align source face to 112x112 using 5-point landmarks + similarity transform
  3. Embed aligned source face into 512-dim vector (ArcFace, norm: (px-127.5)/127.5)
  4. Transform embedding: latent = dot(embedding, emap); latent /= norm(latent)
  5. Align target face to 128x128 for swapping
  6. Swap face (INSwapper, norm: px/255.0)
  7. Blend swapped face back using inverse transform + mask erosion + Gaussian blur

Project Structure

app/src/main/java/com/pv/androidfacefusion/
├── MainActivity.java          UI and image loading
├── FaceFusionProcessor.java   Pipeline orchestrator
├── FaceDetector.java          SCRFD face detection
├── FaceEmbedder.java          ArcFace face embedding
├── FaceSwapper.java           INSwapper face swapping
├── ImageUtils.java            Alignment, transforms, blending
├── ModelDownloader.java       HuggingFace model downloader
└── Face.java                  Face data (bbox, landmarks, embedding)

Requirements

  • Android Studio, Android SDK API 26+
  • Device with ~1 GB free RAM, ~800 MB storage
  • Internet on first launch (model download)

Troubleshooting

Problem Fix
"Failed to load models" Check internet connection and free storage (800+ MB)
"No face detected" Use clear, frontal face images with good lighting
Crash during processing Reduce image size; ensure 1+ GB free RAM
Poor swap quality Verify emap.bin exists in assets (not identity matrix)

Dependencies

License

This project's source code is licensed under the MIT License.

Important: The AI models (SCRFD, ArcFace, INSwapper) downloaded at runtime are provided by InsightFace and are subject to their own non-commercial research license. You must comply with InsightFace's licensing terms when using these models. This project does not redistribute the models — they are downloaded directly from HuggingFace by the end user.

Disclaimer

This project is for educational and research purposes only. Face swapping technology can be misused. By using this software, you agree to:

  • Only use it on images where you have consent from all individuals depicted
  • Not use it to create deceptive, harmful, or non-consensual content
  • Comply with all applicable laws and regulations in your jurisdiction
  • Take full responsibility for how you use the output

The authors are not responsible for any misuse of this software.

Credits