Summary of Pix2gestalt: Amodal Segmentation by Synthesizing Wholes, By Ege Ozguroglu et al.
pix2gestalt: Amodal Segmentation by Synthesizing Wholes
by Ege Ozguroglu, Ruoshi Liu, Dídac Surís, Dian Chen, Achal Dave, Pavel Tokmakov, Carl Vondrick
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research introduces pix2gestalt, a framework for zero-shot amodal segmentation that learns to estimate the shape and appearance of whole objects partially visible behind occlusions. By leveraging large-scale diffusion models and transferring their representations, the authors develop a conditional diffusion model capable of reconstructing entire objects in challenging scenarios, including those that defy natural and physical priors, such as artistic depictions. The framework uses a synthetically curated dataset featuring occluded objects paired with their whole counterparts for training. Experimental results demonstrate that pix2gestalt outperforms supervised baselines on established benchmarks, enabling its use to enhance the performance of existing object recognition and 3D reconstruction methods in the presence of occlusions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to see partially hidden objects without needing any examples from before. They called it pix2gestalt. It uses big models that can generate lots of data and then changes them to work for this new task. The training data comes from making fake pictures of objects with parts missing, paired with the same object fully shown. The results show that pix2gestalt does better than other methods on common tests, which means it can help improve how well computers recognize objects even when some parts are hidden. |
Keywords
* Artificial intelligence * Diffusion model * Supervised * Zero shot
