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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN" "http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en">
<head>
<meta name="keywords"
content="hands,ECCV 2024,workshop,pose estimation,MANO,challenge,keypoint,gesture,robot,grasping,manipulation,hand tracking,motion capture">
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<!--- <title></title> --->
<title>HANDS Workshop</title>
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<body>
<div id="main-container">
<div id="header-container">
<div id="header">
<div id="header-icon-text-container">
<div id="header-text-container">
<nav class="style1">
<ul id="outer_list">
<li id="outer_li_year"><a id="current_year" href="#">2024<span id="arrow"></span></a>
<ul id="top_list">
<li id="style2"><a id="style3" href="workshop2024.html">2024</a></li>
<li id="style2"><a id="style3" href="workshop2023.html">2023</a></li>
<li id="style2"><a id="style3" href="workshop2022.html">2022</a></li>
<li id="style2"><a id="style3" href="https://sites.google.com/view/hands2019/home">2019</a>
<li id="style2"><a id="style3" href="https://sites.google.com/view/hands2018">2018</a>
<li id="style2"><a id="style3" href="">2017</a>
<li id="style2"><a id="style3" href="https://labicvl.github.io/hand/Hands2016/#home">2016</a>
<li id="style2"><a id="style3" href="">2015</a>
</li>
</ul>
</li>
<li id="outer_li"><a id="workshop_link" href="#">Workshop</a>
</li>
<li id="outer_li"><a id="challenge_link" href="#">Challenge</a>
</li>
</ul>
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<div id="layout-content">
<div id="text-img-container">
<div id="img-container">
<a href="https://hands-workshop.org/"><img src="./logos/hands.png" alt="HANDS" width="100%" /></a>
</div>
<div id="text-container"></div>
</div>
<p>
<div id="beamer">
<beam>
Observing and Understanding <b>Hands</b> in Action
</beam></br>
<beams>
in conjunction with ECCV 2024</br>
</beams>
</div>
<br>
<div id="menu-container">
<div id="menu-item"><a id="style6" href="#overview">Overview</a></div>
<div id="menu-item"><a id="style6" href="#schedule">Schedule</a></div>
<div id="menu-item"><a id="style6" href="#papers">Papers</a></div>
<div id="menu-item"><a id="style6" href="#speakers">Speakers</a></div>
<div id="menu-item"><a id="style6" href="#organizers">Organizers</a></div>
<div id="menu-item"><a id="style6" href="#sponsors">Sponsors</a></div>
<div id="menu-item"><a id="style6" href="#contact">Contact</a></div>
</div>
<br>
</p>
<p style="align-items: center;text-align: center; font-size:20px;">
<b>The recordings and the papers are available below</b><br>
</p>
<h1 id="overview">Overview </h1>
<font size="5">
Welcome to our HANDS@ECCV24.
</font>
</br>
</br>
<p>Our HANDS workshop will gather vision researchers working on perceiving hands performing actions, including
2D
& 3D hand detection, segmentation, pose/shape estimation, tracking, etc. We will also cover related
applications including gesture recognition, hand-object manipulation analysis, hand activity understanding,
and
interactive interfaces.</p>
<p>The eighth edition of this workshop will emphasize the use of large foundation models (<i>e.g.</i> CLIP,
Point-E, Segment Anything, Latent Diffusion Models) for hand-related tasks. These models have revolutionized
the
perceptions of AI, and demonstrate groundbreaking contributions to multimodal understanding, zero-shot
learning,
and transfer learning. However, there remains an untapped potential for exploring their applications in
hand-related tasks. </p>
<h1 id="schedule">Schedule (Italy Time)</h1>
<p style="align-items: center;text-align: center;"><b>September 30th (2 pm-6 pm), 2024</b></br>
<b>Room Suite 8, MiCo Milano</b></br>
<b>Poster Boards Position: inside: 11, outside: 11</b></br>
<b>Online Zoom Link: </b><a
href="https://nus-sg.zoom.us/j/9015323166?omn=82071666030">https://nus-sg.zoom.us/j/9015323166?omn=82071666030</a></br>
</p>
<table class="dataintable">
<tbody>
<tr>
<td><b>14:00 - 14:10</b></td>
<td>Opening Remarks</td>
</tr>
<tr>
<td><b>14:10 - 14:40</b></td>
<td> Invited Talk: Hanbyul Joo</td>
</tr>
<tr>
<td></td>
<td> <b>Title:</b> Towards Capturing Everyday Movements to Scale Up and Enrich Human Motion Data</br>
<b>Abstract:</b> In this talk, I will present our lab's efforts to scale and enrich 3D human motion data
by capturing everyday human movements and natural human-object interactions. I will begin by describing
our new multi-camera system, ParaHome, designed to capture human-object interactions in a natural home
environment. Next, I will introduce MocapEve, a lightweight, cost-effective motion capture solution that
uses two smartwatches and a head-mounted camera, enabling full-body 3D motion capture in diverse
settings. Finally, I will discuss our recent work, CHRUS and COMa, which enable machines to model
comprehensive affordances for 3D objects by leveraging pre-trained 2D diffusion models, allowing for
unbounded object interactions.
</td>
</tr>
<tr>
<td colspan="2">
<div class="youtube">
<center>
<iframe width="100%" height="auto" class="elementor-video-iframe"
style="display: block;aspect-ratio:16/9;"
src="https://www.youtube.com/embed/sXMeFW9x32s?si=wi0wURIoSrsMMrvA" frameborder="0"
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
</center>
</iframe>
</div>
</td>
</tr>
<tr>
<td><b>14:40 - 15:10</b></td>
<td> Invited Talk: Shubham Tulsiani</td>
</tr>
<tr>
<td></td>
<td> <b>Title:</b> Understanding Human-object Interactions for Enabling Generalizable Robot
Manipulation</br>
<b>Abstract:</b> We humans continually use our hands to interact with the world around us. From making
our morning coffee to cleaning dishes after dinner, we effortlessly perform a plethora of tasks in our
everyday lives. A central goal in robot learning is to develop similar generalist agents — ones capable
of performing a diverse set of tasks across a wide range of environments. In this talk, I will highlight
some of our recent efforts to build perception systems that better understand human interactions and
allow robots to act in diverse scenarios. I will show how learning a (3D) generative model over
human-object interactions can allow reconstructing interactions from in-the-wild clips, and how (2D)
generative models over human interactions can guide robots acting in the real world.
</td>
</tr>
<tr>
<td colspan="2">
<div class="youtube">
<center>
<iframe width="100%" height="auto" class="elementor-video-iframe"
style="display: block;aspect-ratio:16/9;"
src="https://www.youtube.com/embed/mIz5mMTU0sM?si=MlTSppfzFeE0OEbg" frameborder="0"
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
</center>
</iframe>
</div>
</td>
</tr>
<tr>
<td><b>15:10 - 16:10</b></td>
<td> Coffee break time & Poster </td>
</tr>
<tr>
<td> <b>16:10 - 16:40</b></td>
<td> Invited Talk: Qi Ye</td>
</tr>
<tr>
<td></td>
<td> <b>Title:</b> Understanding Hand-Object Interaction – From human hand reconstruction and generation
to dexterous manipulation of robotic hands</br>
<b>Abstract:</b> In recent years, humanoid robots and embodied intelligence have attracted extensive
attentions. One of the most challenging of endowing humanoid robots with intelligence is human-like
dexterous manipulation with robotic hands. Unlike simple parallel grippers, human-like multi-fingered
hands involve a high degree of freedom and complex variations in hand-object interaction, making it
difficult for humanoid robots to acquire these manipulation skills. This report will explore how to
effectively use human manipulation experience to overcome these challenges and develop and transfer
human-like dexterous manipulation skills. It will cover our recent works on hand-object reconstruction,
grasp generation and motion planning, multi-modal pretraining with human manipulation data for robotic
hands, etc.
</td>
</tr>
<tr>
<td colspan="2">
<div class="youtube">
<center>
<iframe width="100%" height="auto" class="elementor-video-iframe"
style="display: block;aspect-ratio:16/9;"
src="https://www.youtube.com/embed/_f17lZjP_xw?si=ROBMLQIeSR5H899B" frameborder="0"
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
</center>
</iframe>
</div>
</td>
</tr>
<tr>
<td> <b>16:40 - 17:10</b></td>
<td> Invited Talk: Shunsuke Saito</td>
</tr>
<tr>
<td></td>
<td> <b>Title:</b> Foundations for 3D Digital Hand Avatars</br>
<b>Abstract:</b> What constitutes the foundation for 3D digital hand avatars? In this presentation, we
aim to establish the essential components necessary for creating high-fidelity digital hand models. We
argue that relighting, animation/interaction, and in-the-wild generalization are crucial for bringing
high-quality avatars to everyone. We will discuss several relightable appearance representations that
achieve a photorealistic appearance under various lighting conditions. Furthermore, we will introduce
techniques to effectively model animation and interaction priors, and demonstrate how to estimate
complex hand-to-hand and hand-to-object interactions, even with data captured in uncontrolled
environments. Finally, the talk will cover bridging the domain gap between high-quality studio data and
large-scale in-the-wild data, which is key to enhancing robustness and diversity in avatar modeling
algorithms. We will also explore how these foundations can complement and enhance each other.
</td>
</tr>
<tr>
<td colspan="2">
<div class="youtube">
<center>
<iframe width="100%" height="auto" class="elementor-video-iframe"
style="display: block;aspect-ratio:16/9;"
src="https://www.youtube.com/embed/JpEn3Mi4IFE?si=ql-g-MA4qYfW_3J5" frameborder="0"
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
</center>
</iframe>
</div>
</td>
</tr>
<tr>
<td><b>17:10 - 17:25</b></td>
<td> Invited Talk: Prithviraj Banerjee </td>
</tr>
<tr>
<td></td>
<td> <b>Title:</b> HOT3D: A new benchmark dataset for vision-based understanding of 3D hand-object
interactions</br>
<b>Abstract:</b> We introduce HOT3D, a publicly available dataset for egocentric hand and object
tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monochrome
image streams showing 19 subjects interacting with 33 diverse rigid objects, multi-modal signals such as
eye gaze or scene point clouds, as well as comprehensive ground truth annotations including 3D poses of
objects, hands, and cameras, and 3D models of hands and objects.In addition to simple
pick-up/observe/putdown actions, HOT3D contains scenarios resembling typical actions in a kitchen,
office, and living room environment. The dataset is recorded by two head-mounted devices from Meta:
Project Aria, a research prototype of light-weight AR/AI glasses, and Quest 3, a production VR headset
sold in millions of units. Ground-truth poses were obtained by a professional motion-capture system
using small optical markers attached to hands and objects. Hand annotations are provided in the UmeTrack
and MANO formats and objects are represented by 3D meshes with PBR materials obtained by an in-house
scanner.We aim to accelerate research on egocentric hand-object interaction by making the HOT3D dataset
publicly available and by co-organizing public challenges on the dataset. The dataset can be downloaded
from the project website: facebookresearch.github.io
</td>
</tr>
<tr>
<td> <b>17:25 - 17:53</b></td>
<td>
Competition Talks: Team JVhands <br>
<a style="visibility: hidden;"> Competition Talks:</a> Team HCB <br>
<a style="visibility: hidden;"> Competition Talks:</a> Team UVHANDS <br>
<a style="visibility: hidden;"> Competition Talks:</a> Team ACE
</td>
</tr>
<tr>
<td> <b>17:53 - 18:00</b></td>
<td> Closing Remarks</td>
</tr>
</tbody>
</table>
<h1 id="papers">Accepted Papers & Extended Abstracts</h1>
<!-- list papers in the form of:
OakInk2 : A Dataset for Long-Horizon Hand-Object Interaction and Complex Manipulation Task Completion.
Xinyu Zhan*, Lixin Yang*, Kangrui Mao, Hanlin Xu, Yifei Zhao, Zenan Lin, Kailin Li, Cewu Lu.
[pdf] -->
<p>We are delighted to announce the following accepted papers and extended abstracts will appear in the
workshop! All full-length papers, extended abstracts and invited posters should prepare posters for
communication during the workshop.</p> </br>
<p> <b>Poster size: the posters should be portrait (vertical), with a maximum size of 90x180 cm.</b></p>
<!-- <ul> -->
<h2>Full-length Papers</h2>
<ul>
<li> <b>W01</b> AirLetters: An Open Video Dataset of Characters Drawn in the Air <br>
<i>Rishit Dagli, Guillaume Berger, Joanna Materzynska, Ingo Bax, Roland Memisevic</i> <br>
<!-- [pdf] -->
</li>
<a href="files/2024/airletters.pdf">[pdf]</a>
</ul>
<ul>
<li> <b>W02</b> RegionGrasp: A Novel Task for Contact Region Controllable Hand Grasp Generation <br>
<i>Yilin Wang, Chuan Guo, Li Cheng, Hai Jiang</i> <br>
<!-- [pdf] -->
<a href="files/2024/regiongrasp.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W03</b> Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling <br>
<i>Yilin Wen, Hao Pan, Takehiko Ohkawa, Lei Yang, Jia Pan, Yoichi Sato, Taku Komura, Wenping Wang</i> <br>
<!-- [pdf] -->
<a href="files/2024/GHTT_ECCVW_final (1).pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W04</b> Adaptive Multi-Modal Control of Digital Human Hand Synthesis using a Region-Aware Cycle Loss
<br>
<i>Qifan Fu, Xiaohang Yang, Muhammad Asad, Changjae Oh, Shanxin Yuan, Gregory Slabaugh</i> <br>
<!-- [pdf] -->
<a href="files/2024/adaptive.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W05</b> Conditional Hand Image Generation using Latent Space Supervision in Random Variable
Variational Autoencoders <br>
<i>Vassilis Nicodemou, Iason Oikonomidis , Giorgos Karvounas, Antonis Argyros</i> <br>
<!-- [pdf] -->
<a href="files/2024/ECCVW_Hands_2024_CG_SRV_VAE-4.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W06</b> ChildPlay-Hand: A Dataset of Hand Manipulations in the Wild <br>
<i>Arya Farkhondeh*, Samy Tafasca*, Jean-Marc Odobez</i> <br>
<!-- [pdf] -->
<a href="files/2024/childplay-hand.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W07</b> EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos <br>
<i>Masashi Hatano, Ryo Hachiuma, Hideo Saito</i> <br>
<!-- [pdf] -->
<a href="files/2024/emag.pdf">[pdf]</a>
</li>
</ul>
<!-- </ul> -->
<h2>Extended Abstracts</h2>
<ul>
<li> <b>W08</b> AFF-ttention! Affordances and Attention models for Short-Term Object Interaction Anticipation
<br>
<i>Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Jose J Guerrero, Giovanni Maria Farinella, Antonino
Furnari</i> <br>
<!-- [pdf] -->
<a href="files/2024/affordances.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W09</b> Diffusion-based Interacting Hand Pose Transfer <br>
<i>Junho Park*,
Yeieun Hwang*,
Suk-Ju Kang#</i> <br>
<!-- [pdf] -->
<a href="files/2024/IHPT__ECCVW_2024_.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W10</b> Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection? <br>
<i>Rosario Leonardi,
Antonino Furnari,
Francesco Ragusa,
Giovanni Maria Farinella</i> <br>
<!-- [pdf] -->
<a
href="files/2024/[ABSTRACT] Are_Synthetic_Data_Useful_for_Egocentric_Hand_Object_Interaction_Detection.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W11</b> Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer <br>
<i>Xueyi Liu,
Kangbo Lyu,
jieqiong zhang,
Tao Du,
Li Yi</i> <br>
<!-- [pdf] -->
<a href="files/2024/QuasiSim_short_abstract_2.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W12</b> Pre-Training for 3D Hand Pose Estimation with Contrastive Learning on Large-Scale Hand Images
in the Wild <br>
<i>Nie Lin*,
Takehiko Ohkawa*,
Mingfang Zhang,
Yifei Huang,
Ryosuke Furuta,
Yoichi Sato</i> <br>
<!-- add pdf from files/2024 -->
<a href="files/2024/Nie_Lin_HANDS@Workshop_ECCV24_HandCLR_Camera-ready_Submission.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W13</b> Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers <br>
<i>An-Lun Liu, Yu-Wei Chao, Yi-Ting Chen</i> <br>
<!-- [pdf] -->
<a
href="files/2024/ECCV24 Extended Abstracts Submission - Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W14</b> Action Scene Graphs for Long-Form Understanding of Egocentric Videos <br>
<i>Ivan Rodin*, Antonino Furnari*, Kyle Min*, Subarna Tripathi, Giovanni Maria Farinella</i> <br>
<!-- [pdf] -->
<a href="files/2024/EASG___HANDS.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W15</b> Get a Grip: Reconstructing Hand-Object Stable Grasps in Egocentric Videos <br>
<i>Zhifan Zhu, Dima Damen</i> <br>
<!-- [pdf] -->
<a href="files/2024/HANDS_2024_Workshop__Get_a_Grip.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W16</b> Self-Supervised Learning of Deviation in Latent Representation for Co-speech Gesture Video
Generation <br>
<i>Huan Yang, Jiahui Chen, Chaofan Ding, Runhua Shi, Siyu Xiong, Qingqi Hong, Xiaoqi Mo, Xinhan Di</i> <br>
<!-- [pdf] -->
<a href="files/2024/ssl.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W17</b> OCC-MLLM-Alpha:Empowering Multi-modal Large Language Model for the Understanding of Occluded
Objects with Self-Supervised Test-Time Learning <br>
<i>Shuxin Yang, Xinhan Di</i> <br>
<!-- [pdf] -->
<a
href="files/2024/OCC_MLLM_Alpha_Empowering_Multimodal_Large_Language_Model_For_the_Understanding_of_Occluded_Objects.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W18</b> Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera <br>
<i>Zhengdi Yu, Alara Dirik, Stefanos Zafeiriou, Tolga Birdal</i> <br>
<!-- [pdf] -->
<a href="files/2024/Dyn_HaMR_ECCVW_2024_extended_abstract.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W19</b> Learning Dexterous Object Manipulation with a Robotic Hand via
Goal-Conditioned Visual Reinforcement Learning Using Limited Demonstrations <br>
<i>Samyeul Noh, Hyun Myung</i> <br>
<!-- [pdf] -->
<a href="files/2024/learning.pdf">[pdf]</a>
</li>
</ul>
<h2>Invited Posters</h2>
<ul>
<li> <b>W20</b> AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in
the Wild <br>
<i>Junho Park*, Kyeongbo Kong*, Suk-Ju Kang#</i> <br>
<!-- [pdf] -->
<a href="files/2024/attentionhand.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W21</b> HandDAGT : A Denoising Adaptive Graph Transformer for 3D Hand Pose Estimation <br>
<i>Wencan Cheng, Eunji Kim, Jong Hwan Ko</i> <br>
<!-- [pdf] -->
<a href="files/2024/ECCV24_poster_HandDAGT.pdf">[poster]</a>
</li>
</ul>
<ul>
<li> <b>W22</b> On the Utility of 3D Hand Poses for Action Recognition <br>
<i>Md Salman Shamil, Dibyadip Chatterjee, Fadime Sener, Shugao Ma, Angela Yao</i> <br>
<!-- [pdf] -->
<a href="https://arxiv.org/pdf/2403.09805">[pdf]</a>
</li>
</ul>
<ul>
<li> <b>W23</b> ActionVOS: Actions as Prompts for Video Object Segmentation <br>
<i>Liangyang Ouyang, Ruicong Liu, Yifei Huang, Ryosuke Furuta, Yoichi Sato</i> <br>
<!-- [pdf] -->
<a href="files/2024/ActionVOS.pdf">[poster]</a>
</li>
</ul>
<ul>
<li> <b>W24</b> GraspXL: Generating Grasping Motions for Diverse Objects at Scale <br>
<i>Hui Zhang, Sammy Christen, ZicongFan, OtmarHilliges, Jie Song</i> <br>
<!-- [pdf] -->
<a href="files/2024/GraspXL.pdf">[poster]</a>
</li>
</ul>
<h2>Technical Reports</h2>
<ul>
<li> 3DGS-based Bimanual Category-agnostic Interaction Reconstruction <br>
<i>Jeongwan On, Kyeonghwan Gwak, Gunyoung Kang, Hyein Hwang, Soohyun Hwang, Junuk Cha, Jaewook Han,
Seungryul Baek</i> <br>
<!-- [pdf] -->
<a href="files/2024/UVHANDS.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> 2nd Place Solution Technical Report for Hands’24 ARCTIC Challenge from Team ACE <br>
<i>Congsheng Xu*, Yitian Liu*, Yi Cui, Jinfan Liu, Yichao Yan, Weiming Zhao, Yunhui Liu, Xingdong Sheng</i>
<br>
<!-- [pdf] -->
<a href="files/2024/ACE.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li> Solution of Multiview Egocentric Hand Tracking Challenge ECCV2024 <br>
<i>Minqiang Zou, Zhi Lv, Riqiang Jin, Tian Zhan, Mochen Yu, Yao Tang, Jiajun Liang#</i> <br>
<!-- [pdf] -->
<a href="files/2024/JVHANDS.pdf">[pdf]</a>
</li>
</ul>
<ul>
<li>Technical report of HCB team for Multiview Egocentric Hand Tracking Challenge on HANDS 2024 Challenge <br>
<i>Haohong Kuang, Yang Xiao#, Changlong Jiang, Jinghong Zheng, Hang Xu, Ran Wang, Zhiguo Cao, Min Du, Zhiwen
Fang, Joey Tianyi Zhou</i> <br>
<!-- [pdf] -->
<a href="files/2024/HCB.pdf">[pdf]</a>
</li>
</ul>
<!-- <h2>Important Dates (Deadline has been extended)</h2>
<table class="dataintable">
<tbody>
<tr>
<td><b>June 15, 2024 (Opened)</b></td>
<td>Paper Submission Start</td>
</tr>
<tr>
<td><del>July 25</del> <b>August 10, 2024 11:59 (Pacific Time)</b></td>
<td>Full Length Paper and its Supplementary Materials Submission Deadline</td>
</tr>
<tr>
<td><b>August 15, 2024, 11:59 (Pacific Time)</b></td>
<td>CMT for Extended Abstracts & Posters Submission is Closed, Further Submission can be submitted via Email ([email protected])</td>
</tr>
<tr>
<td><del>August 10</del> <b>August 20, 2024, 11:59 (Pacific Time)</b></td>
<td>Full Length Paper Author Notification, Extended Abstracts & Posters Submission Deadline</td>
</tr>
<tr>
<td><del>August 20</del> <b>August 25, 2024, 11:59 (Pacific Time)</b></td>
<td>Camera-ready Submission</td>
</tr>
</tbody>
</table> -->
<!--
<h2>Topics</h2>
We will cover all hand-related topics. The relevant topics include and not limited to:
<ul id="topicstyle1">
<li id="topicstyle2">Hand pose and shape estimation</li>
<li id="topicstyle2">Hand & object interactions</li>
<li id="topicstyle2">Hand detection/segmentation</li>
<li id="topicstyle2">Hand gesture/action recognition</li>
<li id="topicstyle2">4D hand tracking and motion capture</li>
<li id="topicstyle2">Hand motion synthesis</li>
<li id="topicstyle2">Hand modeling, rendering, generation</li>
<li id="topicstyle2">Camera systems and annotation tools</li>
<li id="topicstyle2">Novel algorithms and network architectures</li>
<li id="topicstyle2">Multi-modal learning</li>
<li id="topicstyle2">Self-/un-/weakly-supervised learning</li>
<li id="topicstyle2">Generalization and adaptation</li>
<li id="topicstyle2">Egocentric vision for AR/VR</li>
<li id="topicstyle2">Robot grasping, object manipulation, Haptics</li>
</ul>
-->
<!-- <h2>Submission Guidelines</h2>
<b>Submission Website:</b> <a
href="https://cmt3.research.microsoft.com/HANDS2024">https://cmt3.research.microsoft.com/HANDS2024</a>
</br>
</br>
<p>We accept <b>full length papers</b>, <b>extended abstracts</b> and <b>posters</b> in our workshop. The full
length submissions should be anonymized and will be one-round peer reviewed. The accepted full length papers
will be included in the ECCV24 conference proceedings, while others will only be presented in the workshop. We
welcome papers that are accepted to the ECCV2024 main conference or previous conferences, and extended
abstracts that in progress, to show the posters in our workshop.</p>
<h3>Call for full length papers</h3>
<p>Full length submissions should follow <a
href="https://eccv2024.ecva.net/Conferences/2024/AuthorGuide">ECCV2024 submission policies</a> (no more than
14 pages) and use the official ECCV 2024 template. Note that the submissions violate the double-blind policy
or the dual-submission policy will be rejected without review.</p>
<h3>Call for extended abstracts and posters</h3>
<p>Extended abstracts are not subject to the ECCV rules, and can use other templates. They should be shorter
than the equivalent of 4 pages in CVPR template format. Note that extended abstracts are not anonymized, and
will not be peer reviewed. Accepted extended abstracts will only publish on our website, show in our workshop
and not appear in the ECCV24 conference proceedings. Posters can be simply submitted with PDF and their brief
information like paper title, authors and conference name. </p>
<p><b>The CMT system will close after August 15. After that, extended abstracts and posters can still be submitted via emails ([email protected]) until August 20.</b></p> -->
<h1 id="speakers">Invited Speakers</h1>
<div id="member-container">
<div id="member">
<img src="./profiles/2024/han.jpg">
<p>
<b><a href="https://jhugestar.github.io">Hanbyul Joo</a></b></br>
Seoul National University
</p>
</div>
<div id="member">
<img src="./profiles/2024/prithviraj.png">
<p>
<b><a href="https://scholar.google.com/citations?user=ZmDUIeMAAAAJ&hl=en">Prithviraj Banerjee</a></b></br>
Meta
</p>
</div>
<div id="member">
<img src="./profiles/2024/yeqi.jpg">
<p>
<b><a href="https://person.zju.edu.cn/en/yeqi">Qi Ye</a></b></br>
Zhejiang University
</p>
</div>
<div id="member">
<img src="./profiles/2024/shunsuke.jpg">
<p>
<b><a href="https://shunsukesaito.github.io">Shunsuke Saito</a></b></br>
Reality Labs Research
</p>
</div>
<div id="member">
<img src="./profiles/2024/shubham.jpg">
<p>
<b><a href="https://shubhtuls.github.io">Shubham Tulsiani</a></b></br>
Carnegie Mellon University
</p>
</div>
</div>
<h1 id="organizers">Organizers</h1>
<p>
<div id="member-container">
<div id="member">
<img src="./profiles/2024/hyung.jpg">
<p>
<b><a href="https://hyungjinchang.wordpress.com">Hyung Jin Chang</a></b></br>
University of Birmingham
</p>
</div>
<div id="member">
<img src="./profiles/2024/rongyu.jpg">
<p>
<b><a href="https://gloryyrolg.github.io">Rongyu Chen</a></b></br>
National University of Singapore
</p>
</div>
<div id="member">
<img src="./profiles/2024/zicong.png">
<p>
<b><a href="https://zc-alexfan.github.io">Zicong Fan</a></b></br>
ETH Zurich
</p>
</div>
<div id="member">
<img src="./profiles/2024/otmar.png">
<p>
<b><a href="https://ait.ethz.ch/people/hilliges">Otmar Hilliges</a></b></br>
ETH Zurich
</p>
</div>
<div id="member">
<img src="./profiles/2024/kun.png">
<p>
<b><a href="https://kunhe.github.io">Kun He</a></b></br>
Meta Reality Labs
</p>
</div>
<div id="member">
<img src="./profiles/2024/take.png">
<p>
<b><a href="https://tkhkaeio.github.io/">Take Ohkawa</a></b></br>
University of Tokyo
</p>
</div>
<div id="member">
<img src="./profiles/2024/yoichi.png">
<p>
<b><a href="https://sites.google.com/ut-vision.org/ysato/home">Yoichi Sato</a></b></br>
University of Tokyo
</p>
</div>
<div id="member">
<img src="./profiles/2024/elden.jpg">
<p>
<b><a href="https://eldentse.github.io">Elden Tse</a></b></br>
National University of Singapore
</p>
</div>
<div id="member">
<img src="./profiles/2024/linlin.png">
<p>
<b><a href="https://mu4yang.com">Linlin Yang</a></b></br>
Communication University of China
</p>
</div>
<div id="member">
<img src="./profiles/2024/lixin.png">
<p>
<b><a href="https://lixiny.github.io">Lixin Yang</a></b></br>
Shanghai Jiao Tong University
</p>
</div>
<div id="member">
<img src="./profiles/2024/angela.png">
<p>
<b><a href="https://www.comp.nus.edu.sg/cs/people/ayao/">Angela Yao</a></b></br>
National University of Singapore
</p>
</div>
<div id="member">
<img src="./profiles/2024/linguang.png">
<p>
<b><a href="https://lg-zhang.github.io/">Linguang Zhang</a></b></br>
Facebook Reality Labs (Oculus)
</p>
</div>
</div>
</p>
<h1 id="sponsors">Technical Program Committee</h1>
<p>Thank you so much to the Technical Program Committee for their thoughtful reviews.</p>
<ul id="topicstyle1">
<li id="topicstyle2">Chenyangguang Zhang (Tsinghua University)</li>
<li id="topicstyle2">Gyeongsik Moon (Meta)</li>
<li id="topicstyle2">Jiayin Zhu (NUS)</li>
<li id="topicstyle2">Jihyun Lee (KAIST)</li>
<li id="topicstyle2">Junuk Cha (UNIST)</li>
<li id="topicstyle2">Kailin Li (Shanghai Jiao Tong University)</li>
<li id="topicstyle2">Keyang Zhou (University of Tübingen)</li>
<li id="topicstyle2">Pengzhan Sun (NUS)</li>
<li id="topicstyle2">Rolandos Alexandros Potamias (Imperial College London)</li>
<li id="topicstyle2">Seungryul Baek (UNIST)</li>
<li id="topicstyle2">Takuma Yagi (AIST)</li>
<li id="topicstyle2">Zerui Chen (Inria Paris)</li>
<li id="topicstyle2">Zhiying Leng (Beihang University)</li>
<li id="topicstyle2">Zhongqun Zhang (University of Birmingham)</li>
</ul>
<h1 id="sponsors">Sponsors</h1>
<div class="sponsors-container">
<img class="sponsor-img" src="./profiles/2024/sponsor1.png">
<img class="sponsor-img" src="./profiles/2024/sponsor2_1.svg">
<img class="sponsor-img" src="./profiles/2024/sponsor2_2.png">
</div>
<h1 id="contact">Contact</h1>
<p>[email protected]</p>
<div id="footer">
<p style="align-items: center;text-align: center;">
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