The Problem of Preoperative Visualization
A patient at an aesthetic medicine clinic wants to see the result of rhinoplasty or lip augmentation before the procedure. Surgeons spend up to an hour on manual retouching in Photoshop, and the result isn't always realistic. Our client — a chain of clinics — faced 30% of patients canceling surgery because they couldn't understand how their facial features would change. After implementing an AI system, cancellations dropped by 40%, and consultation time was cut in half. The system generates a photorealistic prediction in minutes with interactive sliders for filler volume, nose shape, and cheekbones. This increases patient trust and reduces staff workload. Contact us to discuss your case.
Which Approach to Choose?
There are three main approaches to prediction. The hybrid pipeline (3DMM + diffusion) offers the best combination of accuracy and realism, so we use it in most projects.
| Approach | Accuracy | Generation Time | Equipment |
|---|---|---|---|
| 3D modeling + PBR | High | 10–30 min | 3D scanner + GPU |
| 2D diffusion | Medium | 30 sec – 2 min | GPU only |
| Hybrid (3DMM + diffusion) | High | 2–5 min | 1–2 photos + GPU |
Predicting Lip Augmentation: From Parameters to Visualization
Lip augmentation with fillers is the most popular procedure. The model uses an extended lip morphable model based on Face 3DMM. Parameters: volume (ml), shape via Bezier curves, cupid's bow definition. After editing parameters, GAN rendering produces photorealism. Our pipeline guarantees preservation of skin texture and natural shine.
Why Rhinoplasty Is the Most Complex Procedure for AI
Changing the nose shape affects the entire face geometry. Parameters: tip projection, bridge height, nostril width. An error in one parameter can make the prediction unrealistic. We solve this through global 3DMM reconstruction with DECA.
# 3DMM reconstruction via DECA
from decalib.deca import DECA
from decalib.utils.config import cfg as deca_cfg
deca = DECA(config=deca_cfg, device='cuda')
image = load_image('patient_photo.jpg') # (1, 3, 224, 224)
with torch.no_grad():
codedicts = deca.encode(image)
# shape: (1, 100) - face shape
# exp: (1, 50) - expression
# pose: (1, 6) - head pose
# Modify nose parameters in shape vector
modified_shape = codedicts['shape'].clone()
modified_shape[0, nose_indices] += delta_nose # delta = desired change
opdict = deca.decode({**codedicts, 'shape': modified_shape})
rendered_image = deca.render(opdict)
Preserving Face Identity
The main challenge with generative approaches is that changing one part may alter the entire identity. Our solution is a two-stage pipeline: first generate a raw prediction, then forcibly preserve the patient's face embedding via IP-Adapter + InstantID. Final check — ArcFace cosine similarity > 0.88 — ensures it's the same person.
Development Stages
- Research and data collection: at least 1000 photos with annotated parameters.
- Pipeline design: from 3DMM reconstruction to final rendering.
- Identity preservation module integration: configure IP-Adapter and custom module.
- Web interface development: photo upload, procedure selection, parameter sliders.
- QA testing: check face symmetry and landmark plausibility.
- Documentation and training: hand over the system to the clinic.
What's Included
- Documentation: architecture description, deployment instructions, API specification.
- Source code: repository with modeling, web interface, tests.
- Staff training: 2–4 sessions on setup and usage.
- Technical support: 3 months post-launch (bug fixes, parameter tuning).
- Demo access: test environment with pretrained models for evaluation before integration.
Example of rhinoplasty parameter tuning: in 3DMM, parameters include nose tip projection (nose_tip_projection), bridge height (bridge_height), and nostril width (nostril_width). For realistic prediction, it's important to set delta limits — the model must not exceed anatomical bounds. Check via mesh plausibility score after decoding.
Development Timeline
| Stage | Duration |
|---|---|
| Basic module (lips, nose, 2D) | 8–12 weeks |
| Full system (3D, multiple procedures) | 16–24 weeks |
Timelines are refined after analyzing your data and requirements.
Important Limitations
The system is intended for consultation and planning, not as a guarantee of results. The actual surgical outcome depends on dozens of factors — tissue condition, healing speed, physician technique. Legally, results are labeled as 'approximate visualization for planning.' Diffusion models sometimes produce anatomically unrealistic results, so a QA step checking face symmetry and landmark plausibility is mandatory.
ROI and Cost
The basic module (lips + nose, 2D approach) takes 8–12 weeks, the full system with 3D reconstruction takes 16–24 weeks. Cost is calculated individually, but the system pays for itself in a few months through reduced cancellations and surgeon time savings. Get a consultation for your case — we will analyze your requirements and propose the optimal solution. Our experience in AI/ML for medicine: over 5 years, 50+ projects in computer vision and generative models. The core of our approach is the 3D Morphable Model (3DMM).