DecorAI

Analyzes uploaded room photographs using computer vision to extract spatial context (dimensions, lighting, existing furniture, architectural features), then conditions a generative image model on these constraints to produce design variations that respect the actual room layout rather than generating abstract designs. The system likely uses object detection and semantic segmentation to identify walls, windows, doors, and existing furnishings, then passes this structured spatial data as conditioning inputs to a diffusion or transformer-based image generation model.

Generates multiple distinct design interpretations of a single room in rapid succession, allowing users to explore different aesthetic directions (minimalist, maximalist, bohemian, industrial, etc.) without re-uploading photos or re-specifying constraints. Likely implements a sampling-based approach where the same room context is passed to the generative model with different style embeddings or prompt variations, enabling parallel generation of diverse outputs.

Allows users to view generated designs overlaid on their actual room using AR technology (smartphone camera), enabling real-time visualization of how the design would look in their space. Likely uses ARKit/ARCore to track the room and overlay the generated design as a virtual layer, with perspective correction to match the user's viewing angle.

Suggests optimal furniture placement and room layout based on spatial constraints, traffic flow, and design principles (e.g., focal points, balance, ergonomics). Likely uses constraint satisfaction or optimization algorithms to find furniture arrangements that maximize usability and aesthetic appeal while respecting room dimensions and existing fixtures.

Tracks user interactions (which designs users save, like, or request modifications to) and builds a preference profile to bias future generations toward their aesthetic tastes. Likely implements a collaborative filtering or embedding-based preference model that learns style affinities from user feedback, then uses these learned preferences to weight the style conditioning in subsequent generation requests.

Extracts furniture, decor items, and materials visible in generated designs and maps them to shoppable products with estimated costs, creating a structured shopping list that users can purchase from integrated e-commerce partners. Likely uses object detection to identify items in the generated image, then queries a product database or API (Amazon, Wayfair, etc.) to find matching items with pricing and availability.

Allows users to request modifications to generated designs through natural language feedback (e.g., 'make it brighter', 'add more plants', 'use warmer colors') without re-uploading photos or starting over. Likely implements a prompt-engineering layer that translates user feedback into conditioning adjustments for the generative model, or uses a fine-tuning approach to adapt the model to user-specific modifications.

Converts 2D generated designs into 3D room models that users can explore interactively, walk through, or import into design software (SketchUp, Blender, etc.). Likely uses depth estimation from the original room photo combined with detected furniture dimensions to reconstruct 3D geometry, then maps the generated design onto this 3D model.

Generates design suggestions that respect user-specified budget constraints, prioritizing cost-effective items and avoiding expensive pieces that exceed the stated budget. Likely integrates pricing data from the shopping-list conversion pipeline into the generation conditioning, using cost as a hard constraint or soft penalty in the model's objective function.

Provides pre-configured design templates optimized for specific room types (bedroom, living room, kitchen, bathroom, home office) with room-type-specific constraints and aesthetic guidelines. Likely uses room classification (detected from the input photo or user-specified) to select appropriate design templates, style palettes, and furniture recommendations that are optimized for that room's function.

Displays the original room photo and generated design side-by-side or in an interactive slider format, allowing users to visually compare the before and after states. Likely uses image alignment and blending techniques to ensure the before/after images are spatially registered for accurate comparison.

Maintains a searchable library of user-generated and AI-generated designs organized by style, room type, color palette, and other metadata. Users can browse, save, and use designs from the library as inspiration or starting points for their own projects. Likely uses semantic search and tagging to enable discovery of similar designs.