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<!DOCTYPE html>
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<h1 class="title is-1 publication-title"><a style="color:black;">HandNeRF</a>: <a style="color:black;">Learning to Reconstruct Hand-Object Interaction Scene from a Single RGB Image</a> </h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
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<img class="img-fluid mb-3" src="static/images/avatar/hongsuk.png" alt="">
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<a href="https://hongsukchoi.github.io/" target="_blank">Hongsuk<br>Choi</a></span>
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<a href="https://nikhilcd.com" target="_blank">Nikhil<br>Chavan-Dafle</a></span>
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<a href="https://kracon7.github.io/" target="_blank">Jiacheng<br>Yuan</a></span>
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<a href="https://cse.umn.edu/cs/volkan-isler" target="_blank">Volkan<br>Isler</a></span>
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<a href="https://www-users.cse.umn.edu/~hspark/" target="_blank">Hyunsoo<br>Park</a>
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<h2 class="subtitle has-text-centered">
<span class="dnerf">HandNeRF</span> reconstructs an unseen semantic neural radiance scene from a single RGB image.
</h2>
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</section>
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Given a single RGB image of a hand-object interaction scene, HandNeRF predicts the hand and object’s density, color, and
semantics, which can be converted to reconstruction of 3D hand and object meshes and rendered to novel view images (RGB,
depth, and semantic segmentation).
<br>
<br>
</div>
<div class="container">
HandNeRF learns the correlation between hand and object geometry from different types
of hand-object interactions, supervised by sparse view images. HandNeRF is tested on a novel scene with an unseen
hand-object interaction. We further demonstrated that object reconstruction from HandNeRF ensures more accurate
execution of downstream tasks, such as grasping and motion planning for robotic hand-over and manipulation.
<br>
<br>
</div>
<div class="container">
<h2 class="title has-text-centered">Why do we use weak-supervision from sparse-view 2D images?</h2>
Acquiring precise 3D object annotations from hand-object interaction scenes is challenging and labor-intensive, not to
mention creating 3D CAD models for each object. Also, since the 3D ground truth itself contains sufficient information
about hand-object interactions, it can readily supervise models regarding interaction priors, such as using contact
information. Instead, we compare models utilizing weak-supervision from easily-obtained, cost-effective sparse-view
images rather than 3D ground truth, to assess the efficacy of different approaches for encoding hand-object interaction
correlations.
<br>
<br>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title has-text-centered">Video</h2>
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<source src="static/videos/HandNeRF.mp4" type="video/mp4">
</video>
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<br>
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<h2 class="title has-text-centered">HandNeRF Method Overview</h2>
<div class="container">
Given a single RGB image of a hand-object interaction scene, HandNeRF predicts the hand and object’s density, color, and
semantics, which can be converted to reconstruction of 3D hand and object meshes and rendered to novel view images (RGB,
depth, and semantic segmentation).
<br>
<br>
</div>
<img src="static/images/method_overview.png" alt="Method"/>
<br>
<br>
<br>
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<p id="centerText">Our Surface-Aware Masking algorithm or SAM</p>
</div>
<center>
<video poster="" id="video1" width="900" height="600" controls muted loop>
<source src="static/videos/SAM_muted.mp4" type="video/mp4">
</video>
</center>
Our Surface-Aware Masking (SAM) algorithm allows us to map unknown 3D areas into 2D accurately. This allows us to then inpaint more accurately which in turn lets us estimate the geometry and texture of the unknown objects in the scene.
</div>
<br> -->
<!-- <br> -->
<!-- <div class="container">
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<p id="centerText">Viewpoint Selection Method</p>
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<source src="static/videos/viewpoints_muted.mp4" type="video/mp4">
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We search for viewpoints by traversing directions along a sphere around the scene away from the original viewpoint. We look for viewpoints using our context ratio (context pixels / all pixels) and use the viewpoint that provides inpainting with enough context to work accurately but still provide new information. We do this for many different directions, and then apply our constistency filtering to obtain our final output.
</div> -->
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<br>
<br>
<h1 class="title has-text-centered"> Downstream tasks</h1>
<br>
<br>
<h2 class="title has-text-centered">Grasp Proposals</h2>
<center>
<div class="container">
Our method enables better grasp proposals based on more accurate 3D object reconstruction.
<br>
<br>
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<img src="static/images/grasp_comparison_cropped-optimize.gif" alt="Method"/>
<br>
<br>
<br>
</center>
<h2 class="title has-text-centered">Motion Planning</h2>
<center>
<div class="container">
Better reconstructed 3D object mesh can be used for collision-free motion planning after the human-to-robot handover of the object.
<br>
<br>
</div>
<img src="static/images/motionplanning.gif" alt="Method"/>
<br>
<br>
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</center>
<center>
<h2 class="title has-text-centered">Realworld HandOver</h2>
<div class="container">
We demonstrate a realworld human-to-robot handover based on our 3D reconstruction.
<br>
<br>
</div>
<img src="static/images/handover_real_cropped-optimized.gif" alt="Method" />
</center>
<br>
</div>
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</section>
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<h2 class="title is-3">Abstract</h2>
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<h2 class="title has-text-centered">Qualitative Novel View Results</h2>
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Our method also has the capability to produce realistic novel views of multiple objects. Using the Rotation + SAM + Inpaint parts of our method, we show qualitative results of our methods ability to generate novel views given an RGB-D image.
<br>
<br>
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<img src="static/images/Inpainted_Qualitative.png" alt="Method"/>
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<h2 class="title">BibTeX</h2>
<pre><code>@InProceedings{choi2024handnerf,
title={HandNeRF: Learning to Reconstruct Hand-Object Interaction Scene from a Single RGB Image},
author={Choi, Hongsuk and Chavan-Dafle, Nikhil and Yuan, Jiacheng and Isler, Volkan and Park, Hyunsoo},
booktitle={International Conference on Robotics and Automation},
year={2024}
}
</code></pre>
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