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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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 ⚑

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

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

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

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

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

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

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

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