Project Vision I am seeking an experienced AI/Computer Vision developer to build a production-grade FaceSwap Engine capable of delivering highly realistic, commercially usable results. This system will serve as the core technology for a future web/app platform, so accuracy, reliability, and scalability are essential. This is not a basic overlay or filter. The goal is a deep-learning based pipeline that respects facial structure, lighting, expression, and skin tone, producing results that look natural and seamless. Core Objectives Input Photo A: Source face Photo B: Target portrait Supported formats: JPG, PNG, WEBP Human faces (single or multiple) Output Requirements The swapped image must: Preserve the source identity accurately Match lighting, shadows, and skin texture Maintain natural expressions and head orientation Avoid distortions, warping, or mismatched tones Deliver high-resolution, artifact-free results Quality should be suitable for: Commercial use Social media content Marketing creatives Professional portrait editing Technical Expectations Preferred Frameworks/Models InsightFace (recommended) SimSwap, FaceShifter, FaceFusion Landmark detection and alignment models Core Pipeline Requirements Face detection Landmark mapping Face alignment and segmentation Masking and seamless blending Color tone and lighting correction Identity preservation Performance Requirements Must handle variations in: Angles Skin tones Lighting environments Image resolutions The system should minimize artifacts even in challenging inputs. Scalability (Preferred) GPU acceleration (CUDA) Batch processing support Modular, API-ready architecture Deliverables Primary Deliverables Fully functional FaceSwap script/function (Python preferred) Production-ready codebase Setup guide with Requirements.txt or environment.yml CLI or simple UI for testing swaps Sample outputs using test images Optional Preferred Deliverables GPU optimization Face enhancement integration (GFPGAN / CodeFormer) Multi-face swap capability API-ready structure (FastAPI or Flask) Code Quality and Ownership Clean, well-documented code architecture No proprietary or locked components Full source code ownership transferred to the client Security and Privacy Must support offline/local processing No mandatory cloud dependency NDA available if required Suggested Milestones Research and model selection Core FaceSwap logic development Blending and color correction module Testing and optimization Final delivery and documentation Who Should Apply Developers with proven experience in: Deep Learning and Computer Vision Python (PyTorch or TensorFlow) Facial recognition or generative models Image processing and blending Prior FaceSwap or related AI projects (strongly preferred) Applicants should provide: Previous work or portfolio samples Demos or GitHub links Proposed approach and estimated timeline Timeline and Budget Timeline: 10 to 21 days Budget: Open to competitive premium proposals Final Goal To build a world-class FaceSwap engine capable of delivering studio-quality, highly realistic results, ready to scale into a full AI product.