  We propose UpFusion, a system that can perform novel view synthesis and infer
3D representations for an object given a sparse set of reference images without
corresponding pose information. Current sparse-view 3D inference methods
typically rely on camera poses to geometrically aggregate information from
input views, but are not robust in-the-wild when such information is
unavailable/inaccurate. In contrast, UpFusion sidesteps this requirement by
learning to implicitly leverage the available images as context in a
conditional generative model for synthesizing novel views. We incorporate two
complementary forms of conditioning into diffusion models for leveraging the
input views: a) via inferring query-view aligned features using a scene-level
transformer, b) via intermediate attentional layers that can directly observe
the input image tokens. We show that this mechanism allows generating
high-fidelity novel views while improving the synthesis quality given
additional (unposed) images. We evaluate our approach on the Co3Dv2 and Google
Scanned Objects datasets and demonstrate the benefits of our method over
pose-reliant sparse-view methods as well as single-view methods that cannot
leverage additional views. Finally, we also show that our learned model can
generalize beyond the training categories and even allow reconstruction from
self-captured images of generic objects in-the-wild.
