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Fangzhou Hong 洪方舟

Ph.D. Student in MMLab@NTU

About Me

Fangzhou Hong is currently a final-year Ph.D. student in the School of Computer Science and Engineering at Nanyang Technological University (MMLab@NTU), supervised by Prof. Ziwei Liu. Previously, he received B.Eng. degree in Software Engineering from Tsinghua University in 2020. His research interests lie on the computer vision and deep learning. Particularly, he is interested in 3D representation learning and its intersection with computer graphics.

News
[2024-02]

Two papers accepted to CVPR 2024.

[2024-01]

One paper accepted to ICLR 2024 (DiffTF).

[2023-12]

Two papers accepted to TPAMI (4D-DS-Net and MotionDiffuse).

[2023-09]

Two papers accecpted to NeurIPS 2023 (one spotlight, one poster).

[2023-08]

We are hosting OmniObject3D challenge.

[2023-07]

Three papers accepted to ICCV 2023.

[2023-05]

I am recognized as CVPR 2023 Outstanding Reviewer.

[2023-01]

One paper (EVA3D) accepted to ICLR 2023 as Spotlight.

[2022-07]

One paper (HuMMan) accepted to ECCV 2022 for Oral presentation.

[2022-05]

One paper (AvatarCLIP) accepted to SIGGRAPH 2022 (journal track).

[2022-03]

One paper (HCMoCo) accepted to CVPR 2022 for Oral presentation.

[2021-09]

One paper (Garment4D) accepted to NeurIPS 2021.

[2021-09]

I am awarded Google PhD Fellowship 2021 (Machine Perception).

[2021-07]

One paper (extended Cylinder3D) accepted by TPAMI.

[2021-03]

Two papers (DS-Net and Cylinder3D) accepted to CVPR 2021.

[2021-01]

Start my journey in MMLab@NTU!

Publications
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SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering

Tao Hu, Fangzhou Hong, Ziwei Liu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024

Dynamic human rendering with the joint modeling of motion dynamics and appearance.

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CityDreamer: Compositional Generative Model of Unbounded 3D Cities

Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024

Unbouned 3D cities generated from 2D image collections!

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Large-Vocabulary 3D Diffusion Model with Transformer

International Conference on Learning Representations (ICLR), 2024

DiffTF achieves state-of-the-art large-vocabulary 3D object generation performance with 3D-aware transformers.

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MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

Mingyuan Zhang*, Zhongang Cai*, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, Ziwei Liu

Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023

The first diffusion-model-based text-driven motion generation framework with probabilistic mapping, realistic synthesis and multi-level manipulation ability.

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Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks

Fangzhou Hong, Lingdong Kong, Hui Zhou, Xinge Zhu, Hongsheng Li, Ziwei Liu

Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023

Extension of the CVPR21 Version; Extend DS-Net to 4D panoptic LiDAR segmentation by the temporally unified instance clustering on aligned LiDAR frames.

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PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation

Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023

PrimDiffusion performs the diffusion and denoising process on a set of primitives which compactly represent 3D humans.

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4D Panoptic Scene Graph Generation

Jingkang Yang, Jun Cen, Wenxuan Peng, Shuai Liu, Fangzhou Hong, Xiangtai Li, Kaiyang Zhou, Qifeng Chen, Ziwei Liu

Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 (Spotlight)

To allow artificial intelligence to develop a comprehensive understanding of a 4D world, we introduce 4D Panoptic Scene Graph (PSG-4D), a new representation that bridges the raw visual data perceived in a dynamic 4D world and high-level visual understanding.

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SHERF: Generalizable Human NeRF from a Single Image

Shoukang Hu*, Fangzhou Hong*, Liang Pan, Haiyi Mei, Lei Yang, Ziwei Liu

International Conference on Computer Vision (ICCV), 2023

Reconstruct human NeRF from a single image in one forward pass!

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DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields

Junzhe Zhang*, Yushi Lan*, Shuai Yang, Fangzhou Hong, Quan Wang, Chai Kiat Yeo, Ziwei Liu, Chen Change Loy

International Conference on Computer Vision (ICCV), 2023

We learn a style field that deforms real 3D faces to styleized 3D faces.

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ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model

International Conference on Computer Vision (ICCV), 2023

ReMoDiffuse is a diffusion-model-based motion generation framework that integrates a retrieval mechanism to refine the denoising process, which enhances the generalizability and diversity.

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EVA3D: Compositional 3D Human Generation from 2D Image Collections

International Conference on Learning Representations (ICLR), 2023 (Spotlight)

EVA3D is a high-quality unconditional 3D human generative model that only requires 2D image collections for training.

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HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling

Zhongang Cai*, Daxuan Ren*, Ailing Zeng*, Zhengyu Lin*, Tao Yu*, Wenjia Wang*, Xiangyu Fan, Yang Gao, Yifan Yu, Liang Pan, Fangzhou Hong, Mingyuan Zhang, Chen Change Loy, Lei Yang, Ziwei Liu

European Conference on Computer Vision (ECCV), 2022 (Oral)

A large-scale multi-modal (color images, point clouds, keypoints, SMPL parameters, and textured meshes) 4D human dataset with 1000 human subjects, 400k sequences and 60M frames.

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AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars

Fangzhou Hong*, Mingyuan Zhang*, Liang Pan, Zhongang Cai, Lei Yang, Ziwei Liu

ACM Transactions on Graphics (SIGGRAPH), 2022

AvatarCLIP empowers layman users to customize a 3D avatar with the desired shape and texture, and drive the avatar with the described motions using solely natural languages.

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Versatile Multi-Modal Pre-Training for Human-Centric Perception

Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Oral)

The first to leverage the multi-modal nature of human data (e.g. RGB, depth, 2D key-points) for effective human-centric representation learning.

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Garment4D: Garment Reconstruction from Point Cloud Sequences

Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu

35th Conference on Neural Information Processing Systems (NeurIPS), 2021

The first attempt at separable and interpretable garment reconstruction from point cloud sequences, especially challenging loose garments.

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LiDAR-based Panoptic Segmentation via Dynamic Shifting Network

Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, Ziwei Liu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021

Rank 1st in the public leaderboard of SemanticKITTI panoptic segmentation (2020-11-16); A learnable clustering module is designed to adapt kernel functions to complex point distributions.

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Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception

Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Wei Li, Yuexin Ma, Hongsheng Li, Ruigang Yang, Dahua Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021

Journal Extension of the CVPR21 version; Extend the cylindrical convolution to more general LiDAR-based perception tasks.

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Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

Xinge Zhu*, Hui Zhou*, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, Dahua Lin

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Oral)

Rank 1st in the public leaderboard of SemanticKITTI semantic segmentation (2020-11-16); Cylindrical 3D convolution is designed to explore the 3D geometric pattern of LiDAR point clouds.

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LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts

Xinhai Liu, Zhizhong Han, Fangzhou Hong, Yu-Shen Liu, Matthias Zwicker

Computer Aided Geometric Design, 2020, 79: 101859. (SCI, 2017 Impact factor: 1.421, CCF B)

To learn discriminative features on point clouds by encoding the fine-grained contexts inside and among local regions simultaneously.

Technical Reports
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DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D Generation

arXiv Preprint, 2024

Extension of our ICLR 2024 paper DiffTF. Joint training of diffusion model and Triplane representation increases the generation quality.

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LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation

arXiv Preprint, 2024

LN3Diff creates high-quality 3D object mesh from text within 8 V100-SECONDS.

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Large Motion Model for Unified Multi-Modal Motion Generation

arXiv Preprint, 2024

Large Motion Model (LMM) is a motion-centric, multi-modal framework that unifies mainstream motion generation tasks into a generalist model.

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StructLDM: Structured Latent Diffusion for 3D Human Generation

Tao Hu, Fangzhou Hong, Ziwei Liu

arXiv Preprint, 2024

StructLDM is a diffusion-based unconditional 3D human generative model learned from 2D images.

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FashionEngine: Interactive Generation and Editing of 3D Clothed Humans

Tao Hu, Fangzhou Hong, Zhaoxi Chen, Ziwei Liu

arXiv Preprint, 2024

FashionEngine is an interactive 3D human generation and editing system with multimodal control (e.g., texts, images, hand-drawing sketches).

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3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors

arXiv Preprint, 2024

Text-to-3D Generation within 5 Minutes! A two-stage design, utilizing both 3D difffusion prior and 2D priors.

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HumanLiff: Layer-wise 3D Human Generation with Diffusion Model

Shoukang Hu, Fangzhou Hong, Tao Hu, Liang Pan, Weiye Xiao, Haiyi Mei, Lei Yang, Ziwei Liu

arXiv Preprint, 2023

We generate 3D digital humans using 3D diffusion model in a controllable, layer-wise way.

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PointHPS: Cascaded 3D Human Pose and Shape Estimation from Point Clouds

Zhongang Cai*, Liang Pan*, Chen Wei, Wanqi Yin, Fangzhou Hong, Mingyuan Zhang, Chen Change Loy, Lei Yang, Ziwei Liu

arXiv Preprint, 2023

SMPL reconstruction from real depth sensor, which are partial point cloud inputs.

Education & Experiences
Surreal, Reality Lab Research, Meta
Redmond, US
Aug. 2023 - Jan. 2024
Research Scientist Intern
MMLab, Nanyang Technological University
Singapore
Jan. 2021 - Present
Ph.D. Student
MMLab, The Chinese University of Hong Kong
Hong Kong, China
Jul. 2020 - Dec. 2020
Research Assistant
SenseTime Group Limited
Beijing, China
Feb. 2019 - Dec. 2019
Research Intern
Tsinghua University
Beijing, China
Aug. 2016 - Jun. 2020
Bachelor Degree in Software Engineering
High GPA 3.93/4.0. Ranking 1/84.
Awards & Scholarships

Google PhD Fellowship 2021

2021

Outstanding Undergraduate Thesis of Tsinghua University

2020

Outstanding Graduate of Tsinghua University

2020

Outstanding Graduate of Beijing

2020

Outstanding Graduate of School of Software, Tsinghua University

2020

ICBC Scholarship (Top 3%)

2019

Hua Wei Scholarship (Top 1%)

2018

Tung OOCL Scholarship (Top 5%)

2017
Invited Talks [Youtube]
Academic Services

Conference Reviewer: CVPR’21/23/24, ICCV’23, NeurIPS’22/23, ICML’23/24, ICLR’24, SIGGRAPH’23, SIGGRAPH Asia’23, AAAI’21/23

Journal Reviewer: TPAMI, IJCV, TCSVT, JABES, PR

Teaching
[2022]

NTU CE/CZ1115 Introduction to Data Science and Artificial Intelligence (Teaching Assistant)

[2022]

NTU CE2003 Digital System Design (Teaching Assistant)

[2021]

NTU CE/CZ1115 Introduction to Data Science and Artificial Intelligence (Teaching Assistant)

[2021]

NTU SC1013 Physics for Computing (Teaching Assistant)