Hang Gao

I am a Ph.D. candidate at UC Berkeley, working on computer vision and graphics, advised by Angjoo Kanazawa.

I did my undergrad at Jiao Tong University and got my master from Columbia. In the past, I have interned at Microsoft Research Asia, Adobe Research and Luma AI.

This summer, I am doing an internship with Varun Jampani at Stability AI on 3D diffusion models.

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 Hang Gao, Zhuoyang Pan, Angjoo Kanazawa In submission, 2024 We recover human avatars from self-occluded internet videos where people only show parts or sides of their body.
	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 / arXiv / code We represent a 3D dynamic scene by 4D gaussians which allows accurate 3D tracking and dynamic-view synthesis.
	NerfAcc: Efficient Sampling Accelerates NeRFs Ruilong Li, Hang Gao, Matthew Tancik, Angjoo Kanazawa ICCV, 2023 project page / arXiv / code We build and release a toolbox for accelerating all kinds of NeRFs by efficient sampling.
	Monocular Dynamic View Synthesis: A Reality Check Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, Angjoo Kanazawa NeurIPS, 2022 project page / arXiv / video / 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 / arXiv / video / 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 / arXiv / 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.