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Chenguo Lin

Ph.D. Student

Wangxuan Institute of Computer Technology (WICT)
School of Intelligence Science and Technology
Peking University
Beijing, People's Republic of China

Email: chenguolin[at]stu.pku.edu.cn

[Google Scholar] [GitHub] [Twitter] [WeChat]

👤 Biography

Hi there👋, my name is Chenguo Lin (in Chinese: 林琛果). I am a fourth-year Ph.D. student of computer science and artificial intelligence at Peking University, China, supervised by Prof. Yadong Mu. My research focuses on advancing the frontier of Multi-modal World Models: AI systems to 👀perceive, 🧠reason about, and 🦾act within the physical world. I pursue this vision through three tightly connected directions: (1) Generative Models for Controllable Video/3D Content, (2) Multi-modality Learning for Visual Reasoning, and (3) Geometry-grounded Perception for Spatial Intelligence. By integrating these three directions, my overarching goal is to develop general-purpose intelligence systems: AI that can see, understand, generate, and interact with complex physical environments.

I'm always open to research discussions and collaborations. Feel free to 📲contact me if you are interested.

📢 News

[2025-09] Two papers about 3D/4D content creation (PartCrafter & DynamicVerse) were accepted to NeurIPS 2025.
[2025-08] A unified 2D & 3D instruction-to-layout generation framework (InstructLayout) was accepted to T-PAMI 2025.
[2025-07] We released a feed-forward model for 4D content creation from monocular videos: MoVieS .
[2025-06] We released a 3D-native DiT that generates 3D objects in parts: PartCrafter .
[2025-01] Two papers about 3D object & dynamics generation (DiffSplat & OmniPhysGS) were accepted to ICLR 2025.
[2024-09] One paper about generalizable single-view human reconstruction (HumanSplat) was accepted to NeurIPS 2024.
[2024-07] One paper about large-scale time-series pretraining (NuTime) was accepted to TMLR 2024.
[2024-01] One paper about 3D scene synthesis (InstructScene) was accepted to ICLR 2024 as a spotlight paper.

📑 Selected Publications [Google Scholar]

*: equal contribution; †: project lead

arXiv 2025	MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second Chenguo Lin, Yuchen Lin, Panwang Pan†, Yifan Yu, Honglei Yan, Katerina Fragkiadaki, Yadong Mu Preprint (arXiv), 2025 [arXiv] [Project Page] [Code] TL;DR: MoVieS is a feed-forward framework that jointly reconstructs appearance, geometry and motion for 4D scene perception from monocular videos.
arXiv 2025	Diff4Splat: Generalizable 4D Scene Generation with Latent Dynamic Reconstruction Models Panwang Pan, Chenguo Lin*, Jingjing Zhao, Chenxin Li, Yuchen Lin, Haopeng Li, Honglei Yan, Kairun Wen, Yunlong Lin, Yixuan Yuan, Yadong Mu Preprint* (arXiv), 2025 [arXiv] [Project Page] TL;DR: Diff4Splat is a generalizable framework for controllable 4D scene generation from a single image using a video diffusion model.
arXiv 2025	ID-Composer: Multi-Subject Video Synthesis with Hierarchical Identity Preservation Panwang Pan, Jingjing Zhao, Yuchen Lin, Chenguo Lin, Chenxin Li, Haopeng Li, Honglei Yan, Tingting Shen, Yadong Mu Preprint (arXiv), 2025 [arXiv] [Project Page] TL;DR: ID-Composer generates multi-subject videos combining text and reference images through identity-preserving attention and VLM-guided semantic alignment.
NeurIPS 2025	PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers Yuchen Lin, Chenguo Lin*, Panwang Pan†, Honglei Yan, Yiqiang Feng, Yadong Mu, Katerina Fragkiadaki Neural Information Processing Systems* (NeurIPS), 2025 Received more than 1.5K GitHub stars within a month [OpenReview] [arXiv] [Project Page] [Code] TL;DR: PartCrafter is a structured 3D generative model that jointly generates multiple parts and objects from a single RGB image in one shot.
NeurIPS 2025	DynamicVerse: Physically-Aware Multimodal Modeling for Dynamic 4D Worlds Kairun Wen, Yuzhi Huang, Runyu Chen, Hui Zheng, Yunlong Lin, Panwang Pan, Chenxin Li, Wenyan Cong, Jian Zhang, Junbin Lu, Chenguo Lin, Dilin Wang, Zhicheng Yan, Hongyu Xu, Justin Theiss, Yue Huang, Xinghao Ding, Rakesh Ranjan, Zhiwen Fan Neural Information Processing Systems (NeurIPS), 2025 [OpenReview] [arXiv] [Project Page] [Code] TL;DR: DynamicVerse is a large-scale multi-modal framework that integrates foundation models to convert videos into 4D representations, such as geometry, motion, semantics, etc.
ICLR 2025	DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation Chenguo Lin, Panwang Pan†, Bangbang Yang, Zeming Li, Yadong Mu International Conference on Learning Representations (ICLR), 2025 [OpenReview] [arXiv] [Project Page] [Code] TL;DR: DiffSplat directly generates 3D Gaussians by taming large text-to-image diffusion models from text prompts and single-view images in 1~2 seconds.
ICLR 2025	OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generation Yuchen Lin, Chenguo Lin†, Jianjin Xu, Yadong Mu International Conference on Learning Representations (ICLR), 2025 [OpenReview] [arXiv] [Project Page] [Code] TL;DR: OmniPhysGS synthesizes general physics-based 3D dynamic scenes, and can automatically and flexibly model various materials with domain-expert constitutive models.
NeurIPS 2024	HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors Panwang Pan, Zhuo Su†, Chenguo Lin, Zhen Fan, Yongjie Zhang, Zeming Li, Tingting Shen, Yadong Mu, Yebin Liu Neural Information Processing Systems* (NeurIPS), 2024 [OpenReview] [arXiv] [Project Page] [Code] TL;DR: HumanSplat digitalizes any human from a single input image in seconds via multi-view image diffusion models and latent Gaussian splatting reconstruction.
T-PAMI 2025	InstructLayout: Instruction-Driven 2D and 3D Layout Synthesis with Semantic Graph Prior Chenguo Lin, Yuchen Lin, Panwang Pan, Xuanyang Zhang, Yadong Mu Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) [IEEE Xplore] [arXiv] [Project Page] [Code] TL;DR: InstructLayout is an extension of InstructScene that improves controllability and fidelity for the layout synthesis of both 2D E-commerce posters and 3D indoor scenes.
ICLR 2024	InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior Chenguo Lin, Yadong Mu International Conference on Learning Representations (ICLR), 2024 Spotlight (acceptance rate: 5%) [OpenReview] [arXiv] [Project Page] [Code] TL;DR: InstructScene is a generative framework to synthesize 3D indoor scenes from textual instructions, and is composed of a semantic graph prior and a layout decoder.
TMLR 2024	NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining Chenguo Lin, Xumeng Wen, Wei Cao, Congrui Huang, Jiang Bian, Stephen Lin, Zhirong Wu† Transactions on Machine Learning Research (TMLR), 2024 [OpenReview] [arXiv] [Code] TL;DR: NuTime is a Transformer-based architecture that can take raw values of time-series data as input without any data normalization and transformation.

🎓 Educations

Ph.D. student, School of Intelligence Science and Technology, Peking University

2022 - present
B.Eng., College of Computer Science, Sichuan University

2018 - 2022
- Comprehensive Ranking: 1/171
- Member of Honor College (for top 2% undergraduates at Sichuan University)

🏆 Honors & Awards

National Scholarship, Ministry of Education (the highest honor scholarship in China)

2025
Outstanding Student, Peking University

2025
Fresh Ph.D. Student Scholarship of WICT, Peking University (only 4 winners at WICT per year)

2022
Outstanding Bachelor Thesis, Sichuan University

2022
BaoSteel Scholarship, BaoSteel Education Foundation (only 6 winnners in SCU per year, including postgraduates)

2021
National Scholarship, Ministry of Education (the highest honor scholarship in China)

2021
Outstanding Graduate, Sichuan University

2021
Outstanding Student (Cadre), Sichuan University

2021,2020,2019
Comprehensive Scholarship, Sichuan University

2021, 2020, 2019

📝 Services

Conference Reviewer:
- Computer Vision: ICCV 2023-2025, CVPR 2025-2026
- Machine Learning: NeurIPS 2023-2025, ICLR 2024-2026, ICML 2024-2025
- Graphics: SIGGRAPH Asia 2025
Journal Reviewer: TMLR, TPAMI, TMM

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