Next-Gen Photorealistic FaceSwap Logic (AI + Computer Vision)
Budget: ₹1,500 – ₹12,500 INR
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.
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.