depthconditioning
Depthconditioning is a technique in computer vision and generative modelling that uses depth information as conditioning input to steer the output of a model, with the goal of producing more accurate geometry and believable perspective in generated content. It is commonly applied to conditional generative systems, such as diffusion models and conditional GANs, where a depth map or depth cues guide the synthesis process.
In practice, depthconditioning can be implemented through several approaches. A simple method is to concatenate the
Depth data used for conditioning can originate from various sources. Real sensors include LiDAR and structured-light
Applications of depthconditioning include 3D-aware image synthesis, view-consistent editing and novel view generation, and AR/VR content
Limitations include reliance on depth map quality, potential noise and missing regions, domain gaps between synthetic