pix2gestalt: Amodal Segmentation by Synthesizing Wholes
Ege Ozguroglu Ruoshi Liu Dídac Surís Dian Chen Achal Dave Pavel Tokmakov Carl Vondrick
We introduce pix2gestalt, a framework for zero-shot amodal segmentation,
which learns to estimate the shape and appearance of whole objects that are
only partially visible behind occlusions. By capitalizing on large-scale
diffusion models and transferring their representations to this task, we learn
a conditional diffusion model for reconstructing whole objects in challenging
zero-shot cases, including examples that break natural and physical priors,
such as art. As training data, we use a synthetically curated dataset
containing occluded objects paired with their whole counterparts. Experiments
show that our approach outperforms supervised baselines on established
benchmarks. Our model can furthermore be used to significantly improve the
performance of existing object recognition and 3D reconstruction methods in the
presence of occlusions.