A proprietary AI image synthesis pipeline that turns two unstructured photographs into a single photorealistic composite. No 3D assets. No depth sensors. No room scans.
Monocular Depth EstimationLight Field AnalysisGenerative CompositingShadow SynthesisTwo-Pass Mirror RenderingText-Conditioned Control
01 / Full Synthesis Pipeline
From Two Photos to One Photorealistic Output
The DecorViz model processes two inputs in a single end-to-end synthesis pipeline. Each stage conditions the next: segmentation feeds compositing, geometry feeds perspective, photometry feeds shadow synthesis.
02 / Stage 1
Robust Product Segmentation
Before compositing begins, the model runs an automatic segmentation stage that isolates the target product from any input image. The pipeline is engineered to handle the full range of real-world inputs without requiring clean or structured source material.
01Clean backgrounds. Product on white, grey, or transparent PNG. Highest fidelity baseline.
02Lifestyle photographs. Product embedded in a styled or staged room scene. Model identifies and extracts the target object from surrounding context.
03E-commerce screenshots. Mixed-content images containing product photography, promotional banners, UI chrome, navigation, and text. The model locates and isolates the product without manual masking.
03 / Stage 2
Monocular Scene Geometry Inference
From a single uncalibrated 2D photograph, the model performs implicit monocular depth estimation and full scene structure analysis. No stereo input, depth sensor, or structured light is required.
01Vanishing point inference. Perspective convergence lines are detected and the dominant vanishing point field is reconstructed to establish scene orientation.
02Floor and ground plane localization. The model identifies the primary support surface: indoor flooring, outdoor patio, decking, or grass. This plane anchors all product placement.
03Per-pixel depth field. A continuous depth map is inferred across the scene, enabling correct depth-sorted placement of inserted objects without collision with existing geometry.
04Spatial scale estimation. Relative object sizes within the scene are analyzed to establish a coherent scale reference for correctly sizing inserted products.
04 / Stage 3
Photometric Environment Analysis
The model performs implicit light field estimation from the scene photograph, recovering the full photometric profile needed to render the inserted product with illumination-consistent fidelity.
Parameter
Description
Light direction
Dominant source angle and azimuth inferred from shadow orientation and highlight distribution across surfaces.
Intensity
Luminance level of the primary light source, determining shadow contrast and specular response on the inserted product.
Color temperature
Kelvin range of the dominant light source recovered from scene white balance. Applied to inserted product surface shading.
Angular distribution
Soft vs. hard light inferred from shadow edge sharpness. Determines shadow penumbra width in synthesis.
05 / Stage 5
Shadow and Occlusion Synthesis
Shadows are geometrically derived from the inferred scene, not applied as static overlays or pre-baked assets. Each shadow component is synthesized independently and composited to match the photometric environment.
Contact Shadow
Soft darkening at the object-to-floor boundary. Radius and intensity derived from inferred light distance.
Ambient Occlusion
Occlusion accumulation in concave regions, corners, and recesses adjacent to the placed object.
Directional Cast
Long-form shadow cast across the floor plane, angled and scaled to match the recovered light source direction.
Penumbra
Shadow edge softness scaled by angular distribution of the light source: hard for direct sun, soft for diffuse ambient.
06 / Reflective Surfaces
Two-Pass Mirror Rendering
When the scene contains mirrors or highly reflective surfaces, the model applies a two-pass rendering approach to generate physically consistent reflections that incorporate the newly placed product.
P1Pass 1: Scene establishment. Room geometry, lighting, and product placement are finalized. The primary composite is rendered without reflective content.
P2Pass 2: Reflective content generation. Mirror surfaces receive reflected content derived from Pass 1 state: the scene geometry, placed product, and lighting are all visible in the reflection with correct perspective inversion and attenuation.
07 / Natural Language Interface
Text-Conditioned Scene Control
An optional natural language prompt allows users to direct specific parameters of the generation. Without a prompt, the model applies fully automatic placement, depth positioning, scale inference, and lighting synthesis. When a prompt is provided, it conditions three independent subsystems:
Spatial Placement
The model performs semantic scene understanding, identifying existing objects and their positions, then resolves spatial relationship instructions.
"put the sofa between the TV and coffee table"
"hang the lamp over the dining table"
Scale Calibration
Natural language dimensional descriptions are parsed and applied to constrain the scale calibration system, improving product-to-room proportionality.
"my living room is 4 meters wide"
"ceiling height is around 2.8m"
Scene Attribute Control
Lighting attributes linked to specific scene elements are parsed and applied to the photometric rendering of the relevant fixture and its scene contribution.
"add warm pink light from the floor lamp"
"scene at golden hour lighting"
08 / Product Fidelity
Material and Appearance Preservation
Material properties, surface texture, and colorimetry of the product are preserved through the synthesis process. The model infers surface type from the product image and applies material-specific light response accordingly. Product geometry and surface appearance are not hallucinated or reinterpreted.
Surface Type
Rendering Behavior
Specular / Lacquered
Specular highlights positioned relative to recovered light source. Environment reflections derived from scene context.
Fabric / Textile
Texture-level shading with directional pile behavior. Subsurface scattering for soft-furnishing materials such as velvet and boucle.
Metal
Anisotropic reflection response. Brushed vs. polished behavior inferred from texture distribution. Environment map contribution applied.
Matte / Painted
Lambertian diffuse shading. Shadow absorption calibrated to surface albedo recovered from product image colorimetry.
Glass / Transparent
Transmission and refraction applied relative to scene background. Fresnel response at silhouette edges.
Natural materials (wood, stone, rattan)
Grain and texture directionality preserved. Diffuse response with low-level specular component derived from surface finish.
09 / Generalization
Scene and Category Coverage
The model generalizes across scene types and product categories without category-specific fine-tuning or 3D scanning. Any product photograph of sufficient clarity is a valid input.
Scene Types
Residential interiors: living rooms, bedrooms, dining, home office