Illumination and Shadows in Head Rotation: experiments with Denoising Diffusion Models

Understand

Accurately modeling the effects of illumination and shadows during head rotation is critical in computer vision for enhancing image realism and reducing artifacts.

• This study delves into the latent space of denoising diffusion models to identify compelling trajectories that can express continuous head rotation under varying lighting conditions.
• A key contribution of our work is the generation of additional labels from the CelebA dataset,categorizing images into three groups based on prevalent illumination direction: left, center, and right.
• These labels play a crucial role in our approach, enabling more precise manipulations and improved handling of lighting variations.