Person signal
Identity, pose, silhouette and visible features anchor the wearer.
ViWear AI
Diffusion,
dressed for reality.
Our generative pipeline brings a person and a garment into the same visual context. It preserves the wearer while synthesizing a plausible new look.
ViWear generation
Context preservedPerson + garment
Diffusion, simply
Generation through
structured refinement.
Noise
→
Structure
→
Detail
→
Result
Diffusion models learn to reverse a corruption process: they start from a noisy representation and progressively recover a coherent image. Latent diffusion performs this work in a compressed visual space, reducing computation while retaining useful detail.
Modern FLUX systems sit in the same generative family but use flow matching. A transformer learns a continuous path from noise toward the target image. For virtual try on, the essential idea is unchanged. Visual references guide the generation instead of a text prompt acting alone.
Why conditioning matters
Not “make an outfit.”
Make this outfit work.
A useful try on system has multiple truths to respect at once. It needs the garment’s design language, the wearer’s identity and body geometry, and enough scene context for the result to feel believable.
Garment signal
Cut, color, texture and local details guide what must be transferred.
Generation prior
The model supplies learned knowledge of bodies, fabric and photographic structure.
Contextual result
A new image that balances fidelity with visual coherence.
The ViWear pipeline
Five stages.
One styling moment.
- 01Input
Prepare the references
The person image and garment asset are normalized so the model receives consistent visual inputs.
- 02Condition
Encode visual meaning
Person and garment representations capture broad semantics alongside detailed appearance cues.
- 03Control
Define the edit
The system establishes which visual regions may change and which identity and scene information should remain stable.
- 04Denoise
Generate by refinement
A finely adapted image transformation model progressively resolves the conditioned latent representation into a coherent try on result.
- 05Review
Validate the result
Outputs are checked for visual quality and product consistency before entering the customer experience.
This is a broad product view. ViWear’s model weights, training data strategy and production orchestration remain proprietary.
The hard part
Photorealism is not enough.
Research into diffusion methods for virtual try on consistently points to the same challenge. The image must look natural without losing the specific identity of the garment or wearer.
Garment fidelity
Small logos, seams, prints and proportions can be washed out by a generator. Conditioning at both semantic and feature levels helps preserve what makes the item specific.
Person preservation
The edit should change clothing while preserving the face, hair, pose and environment. Controlled regions and strong reference signals reduce unwanted drift.
Geometric correspondence
Fabric must follow body shape and pose. Attention mechanisms help align garment information with the correct body regions.
Artifact control
Hands, occlusions, unusual poses and layered garments remain difficult. Quality controls matter as much as the generative model itself.
Our technical direction
A finely adapted image transformation foundation.
ViWear’s proprietary virtual try on work is built around an adapted FLUX architecture for image transformation. The foundation offers strong contextual generation. ViWear adds value by adapting that capability to fashion conditioning, repeatable use and product experiences informed by wardrobe context.
ExperienceStyling informed by wardrobe context
ViWear layerFashion conditioning & controls
FoundationGenerative flow and image transformation
Privacy by design
Personal images stay personal.
ViWear processes user images with explicit consent and does not use them to train AI models.
Explicit consent
Clear user control before images enter the try on flow.
No training use
User images are not repurposed for model training.
European data
Stored in Belgium with retention, access and deletion controls designed around GDPR.
Research foundations