Summary of Geneoh Diffusion: Towards Generalizable Hand-object Interaction Denoising Via Denoising Diffusion, by Xueyi Liu et al.
GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion
by Xueyi Liu, Li Yi
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 paper tackles the challenging problem of denoising hand-object interactions (HOI) by refining erroneous interaction sequences to remove artifacts and create perceptually realistic sequences. The authors propose a novel approach called GeneOH Diffusion, which incorporates two key designs: GeneOH, a contact-centric HOI representation that enhances generalization across various HOI scenarios, and a new domain-generalizable denoising scheme. This scheme consists of a canonical denoising model and a “denoising via diffusion” strategy that can handle input trajectories with various noise patterns. The authors demonstrate the superior effectiveness of their method on four benchmarks with significant domain variations and show promise for various downstream applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about fixing mistakes in how hands interact with objects. When someone does something wrong, like waving their hand around awkwardly, a computer program can try to fix it to make it look more natural. The authors came up with a new way to do this using something called GeneOH Diffusion. It’s like a special filter that makes the mistakes go away and leaves you with a more realistic looking video or animation. |
Keywords
* Artificial intelligence * Diffusion * Generalization
