Depth

Overview

Depth is a image - controlled generation model available on the GenVR platform. Generate photorealistic or stylized images while preserving the spatial structure and composition of a reference image using depth map conditioning. This model interprets depth information to maintain object positioning, perspective, and 3D spatial relationships during the generation process.

Key Features

• Depth map conditioning for spatial consistency and structural preservation
• Maintains camera angles, vanishing points, and perspective accuracy
• Preserves object positioning, scale, and occlusions relationships
• Compatible with photorealistic, illustrative, and abstract artistic styles
• 3D spatial awareness for realistic scene geometry interpretation
• Edge and boundary preservation during style transformation
• Multi-resolution depth processing for flexible input requirements
• Seamless integration with text prompts for detailed scene control

Popular Use Cases

1. Interior design visualization allowing furniture rearrangement while preserving room geometry and perspective
2. Converting 2D concept art into 3D-consistent game environments with maintained spatial relationships
3. Virtual staging for real estate photography with consistent room dimensions and object placement
4. Character redesign and costume variations while maintaining exact poses, proportions, and spatial context
5. Generating seasonal or lighting variations of landscape scenes without altering terrain structure or object placement

Best For

• Architectural visualization and interior design space planning
• Character pose transfer and animation pre-visualization
• Virtual environment creation and consistent game asset generation
• Product photography maintaining exact spatial arrangement and scale
• Style transfer applications requiring strict composition preservation

Limitations to Keep in Mind

• Requires accurate depth map input; low-quality or incorrect depth data results in distorted spatial relationships
• Struggles with transparent, reflective, or translucent surfaces where depth information is ambiguous or missing
• Limited ability to fundamentally alter spatial structure or object distances without generation artifacts
• Complex overlapping objects with similar depths may result in boundary bleeding or spatial confusion
• Output quality heavily depends on the resolution and precision of the input depth map

Why Choose This Model

• Spatial Precision: Maintains exact object positioning and depth relationships from source images without manual alignment.
• Composition Control: Preserves camera angles, perspective lines, and framing across completely different artistic styles.
• Structural Consistency: Ensures generated elements respect the 3D geometry and proportions of the original scene.
• Creative Flexibility: Transform photos into illustrations or vice versa while keeping spatial layout and object placement intact.
• Reduced Prompt Engineering: Achieve complex compositions faster by leveraging existing depth structure rather than relying solely on text descriptions.
• Architecture Accuracy: Perfect for maintaining building proportions, room layouts, and spatial relationships in redesigns and visualizations.
• Pose Preservation: Keeps character poses, gestures, and positioning stable across different rendering styles and environments.
• Scene Recreation: Rebuild environments with new lighting, textures, or seasonal changes without losing spatial structure or perspective.
• Depth-Aware Editing: Modify specific foreground or background elements while preserving the overall spatial hierarchy and occlusion.
• Multi-Style Iteration: Rapidly generate variations in different artistic styles using the same spatial foundation for A/B testing.
• AR/VR Compatibility: Generates content with accurate depth information suitable for augmented reality and spatial computing applications.
• API Efficiency: Optimized for real-time processing and batch workflows, enabling scalable content generation pipelines.
• Cross-Domain Transfer: Convert sketches, 3D renders, or paintings into photorealistic images with preserved spatial relationships.
• Layered Generation: Clearly distinguishes foreground, midground, and background for professional compositing and post-production.
• Perspective Accuracy: Maintains horizon lines and vanishing points essential for architectural and landscape photography.

Alternatives on GenVR

• Canny
• Scribble 2 Img
• Controlnet Preprocessors