Spatial Infill & Adaptive Reuse
Overview:
This research demonstrates an "Adaptive Infill" workflow—leveraging Lineart ControlNet to detect existing spatial boundaries from a raw photograph of an empty interior and simulating various high-fidelity programmatic outcomes.
01. Nordic Minimalist Simulation
- The Control:
ControlNet: Lineart Coarse (Weight 0.45). By isolating the structural edges and window apertures of the empty room, I established a rigid geometric container that prevents spatial distortion during the generation process.
- The Prompt:
Modern apartment, Nordic style, cinematic film still. The focus was on environmental mood-boarding, exploring how soft natural light interacts with minimalist Scandinavian aesthetics.
- The Result:
Environmental Mood-boarding. Using DPM++ 2M with a Karras schedule, I produced a high-detail visualization that respects the original site's architecture while injecting a professional "high-budget" cinematic atmosphere.
02. Retro-Modern Materiality & Masterpiece Furniture
- The Control:
ControlNet: Lineart Coarse. Maintaining the same structural weight (0.45) ensures that the building's skeleton remains constant while allowing the AI to "fill" the space with diverse furniture typologies.
- The Prompt:
Nordic Retro Style, Masterpiece Furniture, shallow depth of field. This experiment moves beyond basic styling into curated interior storytelling, emphasizing high-end materiality and furniture curation.
- The Result:
High-Fidelity Programmatic Testing. The output demonstrates a sophisticated blend of vintage textures and modern lighting, showcasing the ability to rapidly test different brand identities within a single physical site.