SPAD : Spatially Aware Multiview Diffusers
Yash Kant Ziyi Wu Michael Vasilkovsky Guocheng Qian Jian Ren Riza Alp Guler Bernard Ghanem Sergey Tulyakov Igor Gilitschenski Aliaksandr Siarohin
We present SPAD, a novel approach for creating consistent multi-view images
from text prompts or single images. To enable multi-view generation, we
repurpose a pretrained 2D diffusion model by extending its self-attention
layers with cross-view interactions, and fine-tune it on a high quality subset
of Objaverse. We find that a naive extension of the self-attention proposed in
prior work (e.g. MVDream) leads to content copying between views. Therefore, we
explicitly constrain the cross-view attention based on epipolar geometry. To
further enhance 3D consistency, we utilize Plucker coordinates derived from
camera rays and inject them as positional encoding. This enables SPAD to reason
over spatial proximity in 3D well. In contrast to recent works that can only
generate views at fixed azimuth and elevation, SPAD offers full camera control
and achieves state-of-the-art results in novel view synthesis on unseen objects
from the Objaverse and Google Scanned Objects datasets. Finally, we demonstrate
that text-to-3D generation using SPAD prevents the multi-face Janus issue. See
more details at our webpage: https://yashkant.github.io/spad