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# ContactGen: Generative Contact Modeling for Grasp Generation | ||
Shaowei Liu, Yang Zhou, Jimei Yang, Saurabh Gupta*, Shenlong Wang* in ICCV 2023 |
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<meta name="keywords" content="contact, hand-object interaction, grasp synthesis"> | ||
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<title><span style="color: rgb(125, 0, 255);font-weight: bolder;">ContactGen</span>: Generative Contact Modeling for Grasp Generation</title> | ||
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<h1 class="title is-1 publication-title"><span style="color: rgb(125, 0, 255);font-weight: bolder;">ContactGen</span>: Generative Contact Modeling for Grasp Generation</h1> | ||
<div class="is-size-5 publication-authors"> | ||
<span class="author-block"> | ||
<a href="https://stevenlsw.github.io/">Shaowei Liu</a><sup>1</sup>,</span> | ||
<span class="author-block"> | ||
<a href="https://yzhou359.github.io/">Yang Zhou</a><sup>2</sup>,</span> | ||
<span class="author-block"> | ||
<a href="https://jimeiyang.github.io/">Jimei Yang</a><sup>2</sup>,</span> | ||
<span class="author-block"> | ||
<a href="https://saurabhg.web.illinois.edu/"">Saurabh Gupta*</a><sup>1</sup>,</span> | ||
<span class="author-block"> | ||
<a href="https://shenlong.web.illinois.edu/">Shenlong Wang*</a><sup>1</sup> | ||
</span> | ||
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<div class="is-size-5 publication-authors"> | ||
<span class="author-block"><sup>1</sup>University of Illinois Urbana-Champaign,</span> | ||
<span class="author-block"><sup>2</sup>Adobe Research</span><br/> | ||
<span class="authors-block">* Equal Advising</span> | ||
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<span class="author-block">ICCV 2023</span> | ||
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<p> | ||
This paper presents a novel object-centric contact representation <span style="color: rgb(125, 0, 255);font-weight: bolder;">ContactGen</span> for hand-object interaction. | ||
The <span style="color: rgb(125, 0, 255);font-weight: bolder;">ContactGen</span> comprises three components: a contact map indicates the contact location, | ||
a part map represents the contact hand part, | ||
and a direction map tells the contact direction within each part. | ||
Given an input object, we propose a conditional sequential generative model to predict <span style="color: rgb(125, 0, 255);font-weight: bolder;">ContactGen</span> | ||
and adopt model-based optimization to predict diverse and geometrically feasible grasps. | ||
Experimental results demonstrate our method can generate high-fidelity and diverse human grasps for various objects. | ||
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<section class="section" id="BibTeX"> | ||
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<h2 class="title">BibTeX</h2> | ||
<pre><code>@inproceedings{liu2023contactgen, | ||
title={ContactGen: Generative Contact Modeling for Grasp Generation}, | ||
author={Liu, Shaowei and Zhou, Yang and Yang, Jimei and Gupta, Saurabh and Wang, Shenlong}, | ||
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, | ||
year={2023} | ||
} | ||
} | ||
</code></pre> | ||
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