Xingyu Chen (陈星宇)

I am a PhD student at the Inception3D Lab, Westlake University, supervised by Anpei Chen and Andreas Geiger.

My research topics include computer vision, machine learning, and computer graphics, specifically in spatial intelligence.

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headshot
Research

The world we see is constantly changing: how do intelligent systems generalize to new observations? This question led me to quest for an understanding of the mechanisms underlying spatial intelligence and to develop methods for enabling artificial intelligence with this remarkable capability.

Specifically, I am investigating how generalizability can emerge from reusable 3D & 4D representations, how these representations of the dynamic 3D world could be learned from images & videos, and how inductive biases could serve as expert knowledge to reduce unknown parameters and make learning more efficient.

Equal Contribution *, Corresponding Author †, Project Lead ⚑

Feat2GS: Probing Visual Foundation Models with Gaussian Splatting
Yue Chen, Xingyu Chen, Anpei Chen, Gerard Pons-Moll, Yuliang Xiu
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025
project page / arXiv / video / code / demo / gallery

A unified framework to probe "texture and geometry awareness" of visual foundation models. Novel view synthesis serves as an effective proxy for 3D evaluation.

L2G-NeRF: Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields
Yue Chen*, Xingyu Chen*⚑, Xuan Wang†, Qi Zhang, Yu Guo†, Ying Shan, Fei Wang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
project page / arXiv / paper / code / supplementary / video / poster

We combine local and global alignment via differentiable parameter estimation solvers to achieve robust bundle-adjusting Neural Radiance Fields.

UV Volumes for Real-time Rendering of Editable Free-view Human Performance
Yue Chen*, Xuan Wang*, Xingyu Chen, Qi Zhang, Xiaoyu Li, Yu Guo†, Jue Wang, Fei Wang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
project page / arXiv / paper / code / supplementary / video / poster

We separate high-frequency human appearance from 3D volume and encode them into a 2D texture, which enables real-time rendering and retexturing.

Ha-NeRF😆: Hallucinated Neural Radiance Fields in the Wild
Xingyu Chen, Qi Zhang†, Xiaoyu Li, Yue Chen, Ying Feng, Xuan Wang, Jue Wang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
project page / arXiv / paper / supplementary / code / video / poster

We recover NeRF from tourism images with variable appearance and occlusions, and consistently render free-occlusion views with hallucinated appearances.


Invited Talks
Inferring the physical world and camera poses from images
ETH Zurich, 2023

Sharing the intuition of dealing with dynamic objects in our previous work and give a prospect of handling the tracking problem via neural fields.

光影幻象:神经辐射场中的时空流转
Neural Radiance Fields for Unconstrained Photo Collections

深蓝学院 (Shenlan College online education), 2022

Introduction about Neural Radiance Fields (NeRF) for unconstrained photo collections. Including NeRF, NeRF in the Wild, and Ha-NeRF

Academic Services
  • Reviewer of Computer Vision Conferences: CVPR, ICCV, ECCV, 3DV
  • Reviewer of Machine Learning Conferences: NeurIPS, ICLR, ICML

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