DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows

Understand

We introduce a novel approach to single-view face relighting in the wild, addressing challenges such as global illumination and cast shadows.

• A common scheme in recent methods involves intrinsically decomposing an input image into 3D shape, albedo, and lighting, then recomposing it with the target lighting.
• However, estimating these components is error-prone and requires many training examples with ground-truth lighting to generalize well.
• Our work bypasses the need for accurate intrinsic estimation and can be trained solely on 2D images without any light stage data, relit pairs, multi-view images, or lighting ground truth.