Research
I am interested in modeling 3D dynamics in-the-wild. I
have been working on non-rigid reconstruction and neural
rendering. Recently, I start to explore and train
generative priors from 3D data and videos.
SOAR: Self-Occluded Avatar Recovery from a Single
Video
Zhuoyang Pan* ,
Angjoo Kanazawa ,
Hang Gao*
In submission , 2024
We recover human avatars from self-occluded internet
videos where people only show parts or sides of their
body.
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Shape of Motion: 4D Reconstruction from a Single
Video
Qianqian Wang* ,
Vickie Ye* , Hang Gao* ,
Jake Austin , Zhengqi Li ,
Angjoo Kanazawa
arXiv , 2024
project page
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arXiv
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code
We represent a 3D dynamic scene by 4D gaussians which
allows accurate 3D tracking and dynamic-view synthesis.
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NerfAcc: Efficient Sampling Accelerates
NeRFs
Ruilong Li ,
Hang Gao ,
Matthew Tancik ,
Angjoo Kanazawa
ICCV , 2023
project page
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arXiv
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code
We build and release a toolbox for accelerating all kinds
of NeRFs by efficient sampling.
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Monocular Dynamic View Synthesis: A Reality
Check
Hang Gao ,
Ruilong Li ,
Shubham Tulsiani ,
Bryan Russell ,
Angjoo Kanazawa
NeurIPS , 2022
project page
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arXiv
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video
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code
We show a discrepancy between the practical captures and
the existing experimental protocols in dynamic view
synthesis from monocular video.
Long-term Human Motion Prediction with Scene
Context
Zhe Cao ,
Hang Gao ,
Karttikeya Mangalam ,
Qi-Zhi Cai , Minh Vo ,
Jitendra Malik
ECCV , 2020  
(Oral Presentation)
project page
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arXiv
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video
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code
We predict long-term, diverse human motion in 3D by
understanding scene context from an image.
Deformable Kernels: Adapting Effective Receptive Fields
for Object Deformation
Hang Gao* ,
Xizhou Zhu* ,
Steve Lin ,
Jifeng Dai
ICLR , 2020
project page
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arXiv
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code
By learning an instance-adaptive convolutional operator
through 2D deformation in kernel space, we can adapt the
effective receptive field at runtime.
Spatio-Temporal Action Graph Networks
Roei Herzig* ,
Elad Levi* ,
Huijuan Xu* , Hang Gao , Eli Brosh,
Xiaolong Wang ,
Amir Globerson ,
Trevor Darrell
ICCV Workshop , 2019
arXiv
We model video as a spatial-temporal relational graph for
action recognition and find that the second order affinity
(affinity between edges) is surprisingly helpful.
Disentangling Propagation and Generation for Video
Prediction
Hang Gao* ,
Huazhe Xu ,
Qi-Zhi Cai ,
Ruth Wang , Fisher Yu ,
Trevor Darrell
ICCV , 2019
arXiv
High fidelity video prediction is easier if we disentangle
the flow propagation from frame generation.
Low-shot Learning via Covariance-Preserving Adversarial
Augmentation Networks
Hang Gao ,
Zheng Shou ,
Alireza Zareian ,
Hanwang Zhang ,
Shih-Fu Chang
NeurIPS , 2018
arXiv
We use learned feature augmentation to train low-shot
classifiers.
AutoLoc: Weakly-supervised Temporal Action Localization
in Untrimmed Videos
Zheng Shou ,
Hang Gao ,
Lei Zhang ,
Kazuyuki Miyazawa ,
Shih-Fu Chang
ECCV , 2018
arXiv
/
code
We propose a weakly-supervised method for temporal action
localization by maximizing the difference inside and
outside the localization box.
ER: Early Recognition of Inattentive Driving Events
Leveraging Audio Devices on Smartphones
Xiangyu Xu ,
Hang Gao ,
Jiadi Yu ,
Yingying Chen ,
Yanmin Zhu ,
Guangtao Xue ,
Minglu Li
INFOCOM , 2017
IEEE
We developed a audio-based early recognition system for
inattentive driving events through Doppler effect.
Yet another
Jon Barron website
(with minor tweaks).
Last updated Jun 2024.