Amodal Completion via Progressive Mixed Context Diffusion

1 University of Pennsylvania  2Adobe Inc.
CVPR 2024 Highlight

Our method can recover the hidden pixels of objects in diverse images. Occluders may be co-occurring (a person on a surfboard), accidental (a cat in front of a microwave), the image boundary (giraffe), or a combination of these scenarios.
The pink outline indicates an occluder object.

Skip to:    [Abstract]   [Progressive Occlusion-aware Completion]   [Mixed Context Diffusion Sampling]   [Results]   [Cite]  


Our brain can effortlessly recognize objects even when partially hidden from view. Seeing the visible of the hidden is called amodal completion; however, this task remains a challenge for generative AI despite rapid progress. We propose to sidestep many of the difficulties of existing approaches, which typically involve a two-step process of predicting amodal masks and then generating pixels. Our method involves thinking outside the box, literally! We go outside the object bounding box to use its context to guide a pre-trained diffusion inpainting model, and then progressively grow the occluded object and trim the extra background. We overcome two technical challenges: 1) how to be free of unwanted co-occurrence bias, which tends to regenerate similar occluders, and 2) how to judge if an amodal completion has succeeded. Our amodal completion method exhibits improved photorealistic completion results compared to existing approaches in numerous successful completion cases. And the best part? It doesn't require any special training or fine-tuning of models.

Progressive Occlusion-aware Completion


Mixed Context Diffusion Sampling

mixed context


Amodal Completions


Our method completes objects within and beyond the image boundary.

Comparisons with Prior Works


Comparisons of our method with prior works on natural images.


    title={Amodal Completion via Progressive Mixed Context Diffusion},
    author={Xu, Katherine and Zhang, Lingzhi and Shi, Jianbo},