Publication
CAMEO: Correspondence-Attention Alignment for Multi-View Diffusion Models
A training technique that supervises attention maps using geometric correspondence, reducing training iterations by half while achieving superior multi-view generation quality.
Abstract
We present CAMEO, a method to align correspondence and attention for multi-view diffusion models. By supervising attention maps with geometric correspondence during training, we ensure that attention across different views remains consistent with underlying 3D geometry. This achieves significantly improved multi-view consistency while reducing training iterations by half.