DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models

In recent years, generative models have undergone significant advancement due to the success of diffusion models. The success of these models is often attributed to their use of guidance techniques, such as classifier and classifier-free methods, which provides effective mechanisms to trade-off between fidelity and diversity. However, these methods are not capable of guiding a generated image to be aware of its geometric configuration, e.g., depth, which hinders the application of diffusion models to areas that require a certain level of depth awareness. To address this limitation, we propose a novel guidance approach for diffusion models that uses estimated depth information derived from the rich intermediate representations of diffusion models. To do this, we first present a label-efficient depth estimation framework using the internal representations of diffusion models. At the sampling phase, we utilize two guidance techniques to self-condition the generated image using the estimated depth map, the first of which uses pseudo-labeling, and the subsequent one uses a depth-domain diffusion prior. Experiments and extensive ablation studies demonstrate the effectiveness of our method in guiding the diffusion models toward geometrically plausible image generation. Project page is available at https://ku-cvlab.github.io/DAG/.