We are building a next-generation AI-powered fashion platform and need an experienced 3D / Computer Vision engineer to build a Doji-style avatar generation system with high consistency across: Identity likeness Pose normalization Body density / proportions Multi-session regeneration stability This is NOT a basic cartoon avatar project. We are aiming for a commercially deployable, scalable avatar pipeline suitable for a fashion-tech product. We are looking for someone who understands 3D human modeling, parametric body models, mesh optimization, and consistent avatar reconstruction from user inputs. Create a system that generates: A stylized (Doji-like) avatar From user inputs (photos, measurements, or both) With consistent pose, body proportions, and mesh density Suitable for fashion try-on, outfit simulation, and rendering Exportable as GLB/GLTF for app integration We have already explored: Parametric face modeling (DECA-level identity reconstruction) Landmark-based fitting (face_alignment, mediapipe) Multi-view fitting attempts Placeholder UV mapping → discovered texture distortion issues Basic GLB exports Pose normalization challenges Identity drift across regeneration sessions Inconsistent body mesh density between generations Variation in pose when user uploads different images Inconsistent topology when regenerating the same user Core Problems We Need Solved 1️⃣ Identity Consistency If a user generates their avatar multiple times: Face identity must remain stable Body proportions must not change Mesh topology should remain consistent No random density variations We need deterministic or near-deterministic generation. 2️⃣ Pose Normalization Regardless of input photo pose: Output avatar must be in standardized A-pose or T-pose No leaning / asymmetric shoulders No camera distortion baked into mesh We need: Pose extraction Canonical re-projection Pose-independent body reconstruction 3️⃣ Body Density & Topology Consistency We need: Fixed vertex count Stable mesh topology Consistent UV layout Production-ready mesh for clothing fitting This is critical for: Outfit draping Simulation Size estimation Vendor interoperability 4️⃣ User Input Pipeline We want flexibility in user input: Possible inputs: 2–4 photos (front, side, optional back) Height Weight Gender selection Optional body measurements Optional LiDAR capture (future phase) We need guidance on: Minimum viable input set Tradeoffs between measurement-based vs photo-based reconstruction How to reduce user friction while preserving accuracy 🧠 Technical Direction (Open to Improvement) We are open to approaches such as: SMPL / SMPL-X based pipeline Neural body reconstruction with canonical mapping Parametric body + stylized retargeting Face identity embedding + body param fitting Hybrid photogrammetry + parametric modeling Differentiable rendering pipelines We want expert recommendations. 📦 Deliverables End-to-end avatar generation pipeline Consistent mesh topology GLB export Clean UV mapping Texture baking pipeline Pose normalization system Identity consistency across sessions Documentation of architecture Recommendations for scaling to production Bonus: Real-time generation optimization Backend-ready containerization Vendor SDK architecture suggestions 🧑‍💼 Ideal Candidate You have experience in: 3D human reconstruction SMPL / SMPL-X / STAR models Computer vision Mesh processing UV unwrapping GLTF/GLB export Python + PyTorch Blender automation Differentiable rendering Avatar systems AR / fashion tech preferred You must demonstrate: Previous avatar or human reconstruction work Strong understanding of pose normalization Understanding of deterministic mesh topology Experience preparing meshes for clothing simulation