Jing Tan (谈婧)

I'm a third-year Ph.D. Candidate in MMLab at The Chinese University of Hong Kong, supervised by Prof. Dahua Lin. I work on scene-level visual creation and manipulation using structured representations and multimodal generative models.

Previously, I obtained the B.Sc degree from Nanjing University in 2020, and the Master degree from Nanjing University under the supervision of Prof. Limin Wang in 2023.

I was an applied scientist intern at AWS AI Lab during the summer of 2025, where I collaborated with Prof. Zhuowen Tu and Prof. Jiajun Wu.

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profile photo

Research (Google Scholar)

In my research, I consistently leverage the inherent structure of visual data. In video understanding, I exploit the natural temporal and semantic structure of videos to enable efficient perception. In visual scene generation, I study how structured scene representations support reliable and controllable creation and manipulation of visual content.

Imagine360: Immersive 360 Video Generation from Perspective Anchor
Jing Tan*, Shuai Yang*, Tong Wu, Jingwen He, Yuwei Guo, Ziwei Liu, Dahua Lin
NeurIPS, 2025 (* equal contribution)
project page / code / video / arXiv

Imagine360 lifts standard perspective video into 360-degree video with rich and structured motion, unlocking dynamic scene experience from full 360 degrees.
LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation
Shuai Yang*, Jing Tan*, Mengchen Zhang, Tong Wu, Yixuan Li, Gordon Wetzstein, Ziwei Liu, Dahua Lin
SIGGRAPH (Conference Track), 2025 (* equal contribution)
project page / code / video / arXiv

LayerPano3D generates full-view, explorable panoramic 3D scene from a single text prompt.
Temporal Perceiver: A General Architecture for Arbitrary Boundary Detection
Jing Tan, Yuhong Wang, Gangshan Wu, Limin Wang
T-PAMI, 2023
arXiv / code / blog

We present Temporal Perceiver (TP), a general architecture based on Transformer decoders as a unified solution to detect arbitrary generic boundaries, including shot-level, event-level and scene-level temporal boundaries.
PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points
Jing Tan, Xiaotong Zhao, Xintian Shi, Bin Kang, Limin Wang
NeurIPS, 2022
arXiv / code / blog

PointTAD effectively tackles multi-label TAD by introducing a set of learnable query points to represent the action keyframes.
Relaxed Transformer Decoders for Direct Action Proposal Generation
Jing Tan*, Jiaqi Tang*, Limin Wang, Gangshan Wu
ICCV, 2021 (* equal contribution)
pdf / code / blog

The first transformer-based framework for temporal action proposal generation.

Here are some collaborative works on 3D object generation and video generation related topics.

SS4D: Native 4D Generative Model via Structured Spacetime Latents
Zhibin Li*, Mengchen Zhang*, Tong Wu, Jing Tan, Jiaqi Wang, Dahua Lin
SIGGRAPH Asia (TOG), 2025
project page / arXiv / code

SS4D is a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video.
GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography
Mengchen Zhang, Tong Wu, Jing Tan, Ziwei Liu, Gordon Wetzstein, Dahua Lin
ICCV, 2025
project page / arXiv / code

GenDoP is an auto-regressive model that generate artistic and expressive camera trajectories from text prompts and geometric cues.
IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations
Zhibin Li, Tong Wu, Jing Tan, Mengchen Zhang, Jiaqi Wang, Dahua Lin
ICLR, 2025
project page / arXiv / code / data

IDArb is a Diffusion-based intrinsic decomposition framework for an arbitrary number of image inputs under varying illuminations.
HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation
Zhenzhi Wang, Yixuan Li, Yanhong Zeng, Youqing Fang, Yuwei Guo, Wenran Liu, Jing Tan, Kai Chen, Tianfan Xue, Bo Dai, Dahua Lin
NeurIPS D&B Track, 2024
project page / arXiv / code

Camera-controllable human image animation dataset and framework.
Dual DETRs for Multi-Label Temporal Action Detection
Yuhan Zhu, Guozhen Zhang, Jing Tan, Gangshan Wu, Limin Wang
CVPR, 2024
arXiv / code

A new Dual-level query-based TAD framework to precisely detect actions from both instance-level and boundary-level.

Experience
AWS AI Lab
Applied Scientist Intern
June 2025 – Nov. 2025
Bellevue, WA, US
  • Conducted research in collaboration with Prof. Zhuowen Tu and Prof. Jiajun Wu on reinforcement learning–guided spatial-aware image editing, with Yantao Shen as the internship manager.
  • Leveraged RLVR to enable precise geometric object transformations in images following text instructions.
Tencent, PCG
Research Intern
Dec. 2021 – Mar. 2023
Beijing, China
  • Worked on multi-label temporal action detection via learnable query points.
  • Developed a more general video action detection framework capable of localizing specific actions among multiple simultaneous actions.

Selected Honors & Awards

  • CSIG Master’s Thesis Incentive Program Awardee, China Society of Image and Graphics, 2025
  • Outstanding Master’s Thesis Award (3/226), Nanjing University, 2024
  • National Scholarship (6/226), Ministry of Education of China, 2022
  • 3rd Place, GEBD-LOVEU Challenge @ CVPR Workshop, 2022

Academic Services

  • Regular Conference reviewer for CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR; Reviewer for SIGGRAPH 2025.
  • Journal reviewer for IJCV and TIP.
  • Teaching Assistant for IERG4998 and IERG4999 at CUHK.



Thanks Jon Barron for sharing the source code of this website template.