Realistic AI Product Visualization Model

Project Title: AI-Based Warping Model with Diffusion Refinement Project Overview: I am looking for an AI/ML expert to build a warping model that can deform objects realistically onto target surfaces (similar to virtual try-on models) and refine them using a diffusion-based approach for photorealistic quality. The model should be able to: 1. Warp an object using flow-based techniques (e.g., Thin Plate Spline (TPS), Dense Optical Flow, or a CNN-based Warping Field). 2. Enhance and refine the warped image using Stable Diffusion with ControlNet (Canny, Depth, or Optical Flow-based guidance). 3. Optionally, train both models end-to-end for better consistency. This system will be used for realistic virtual product visualization, such as clothing try-ons, furniture placement, or product customizations. Project Scope & Deliverables: The freelancer will be responsible for: 1. Warping Model Development (Flow-Based Warping) • Implement a warping model that takes an input object and warps it onto a target shape. • Options: • Thin Plate Spline (TPS) • Dense Optical Flow (RAFT/PWC-Net) • Custom CNN-based warping model • Train the warping model on a dataset of (input, warped) image pairs. 2. Diffusion-Based Refinement • Use Stable Diffusion (SD 1.5 or SDXL) + ControlNet to enhance warped images. • Fine-tune the diffusion model using custom dataset. • Explore ControlNet variations (e.g., Canny, Depth, or Flow-based guidance). 3. Integration & Optimization • Combine the warping model and diffusion model for end-to-end processing. • Ensure fast inference speed (using optimization techniques like TensorRT, ONNX, or model distillation). 4. Deployment (Optional) • Package the solution into a FastAPI/Flask service with an API for image upload & processing. • Deploy on AWS, GCP, or a local GPU server. Required Skills: • Deep Learning & Computer Vision: PyTorch, TensorFlow • Image Warping & Optical Flow: TPS, RAFT, Dense Warping Fields • Diffusion Models & ControlNet: Stable Diffusion, Diffusers Library • API Development (Optional): FastAPI, Flask, Docker Project Timeline & Budget: • Estimated Duration: 4-6 weeks • Budget: 8000 - 10000 (negotiable based on expertise & scope) How to Apply: Interested freelancers should: 1. Share their past work in image warping, virtual try-on, or diffusion models. 2. Provide a brief technical approach on how they would build the warping model. 3. Mention if they can deploy the model as an API.