A diffusion-based framework generates aligned novel views of images and geometry using warping-and-inpainting with cross-modal attention distillation and proximity-based mesh conditioning, achieving high-fidelity synthesis and 3D completion.
We introduce a diffusion-based framework that performs aligned novel view
image and geometry generation via a warping-and-inpainting methodology. Unlike
prior methods that require dense posed images or pose-embedded generative
models limited to in-domain views, our method leverages off-the-shelf geometry
predictors to predict partial geometries viewed from reference images, and
formulates novel-view synthesis as an inpainting task for both image and
geometry. To ensure accurate alignment between generated images and geometry,
we propose cross-modal attention distillation, where attention maps from the
image diffusion branch are injected into a parallel geometry diffusion branch
during both training and inference. This multi-task approach achieves
synergistic effects, facilitating geometrically robust image synthesis as well
as well-defined geometry prediction. We further introduce proximity-based mesh
conditioning to integrate depth and normal cues, interpolating between point
cloud and filtering erroneously predicted geometry from influencing the
generation process. Empirically, our method achieves high-fidelity
extrapolative view synthesis on both image and geometry across a range of
unseen scenes, delivers competitive reconstruction quality under interpolation
settings, and produces geometrically aligned colored point clouds for
comprehensive 3D completion. Project page is available at
https://cvlab-kaist.github.io/MoAI.
