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 map as an additional input channel alongside color information. More advanced techniques use conditioning mechanisms such as feature-wise linear modulation (FiLM) or cross-attention to integrate depth information into intermediate representations. These methods enable the model to respect depth structure when rendering textures, occlusions, and object boundaries.

Depth data used for conditioning can originate from various sources. Real sensors include LiDAR and structured-light systems, while depth maps can also be obtained through stereo reconstruction or monocular depth estimation. During training, models may use paired RGB-depth datasets or syntheticDepth maps to learn the association between depth structure and appearance.

Applications of depthconditioning include 3D-aware image synthesis, view-consistent editing and novel view generation, and AR/VR content creation. It is also explored for robotics simulation and other domains where geometric consistency across views is important.

Limitations include reliance on depth map quality, potential noise and missing regions, domain gaps between synthetic and real data, and increased computational complexity. See also depth map, depth estimation, diffusion models, and FiLM.