Yuxuan Xue

Hi There! I am a Ph.D. student in the Real Virtual Human group at University of Tuebingen , supervised by Prof. Dr. Gerard Pons-Moll. I affiliate with International Max-Planck Research School for Intelligent Systems (IMPRS-IS).

Prior to that, I spent wonderful time in Max-Planck-Institute for Intelligent Systems. I graduated with the double Master degree in Mechanical Engineering as well as Robotics both with distinction from the Technical University of Munich (TUM) in 2022. I received the Bachelor degree in Mechanical Engineering from the same University in 2020.

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News & Award

[2024-06] Our Paper Human 3Diffusion is available on Arxiv .
[2024-02] Our Paper E-LnR is accepted to IJCV 2024.
[2024-01] Our Paper BOFT is accepted to ICLR 2024.
[2023-09] Our Paper OFT is accepted to NeurIPS 2023.
[2023-07] Our Paper NSF is accepted to ICCV 2023.
[2022-11] I am honored to receive the Best Student Paper Award from the BMVC 2022.
[2022-06] I received my M.Sc degree from TUM with distinction.
Human 3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models
Yuxuan Xue, Xianghui Xie, Riccardo Marin, Gerard Pons-Moll
Arxiv 2024
BibTex / Arxiv / Website / Code

We propose a new approach to reconstruct high-fidelity avatar in 3D Gaussian Splats from single RGB Image. Our approach improves 2D multi-view diffusion process by using reconstructed 3D representation to guarantee 3D consistency at reverse sampling steps.

E-LnR: Event-Based Non-rigid Reconstruction of Low-Rank Parametrized Deformations from Contours
Yuxuan Xue, Haolong Li, Stefan Leutenegger, Jörg Stückler.
IJCV 2024, Open Access
BibTex / Website

We propose E-LnR, an event-based approach which reconstruct non-rigid deformation in the low rank parametrized space. This is a journal extension of our BMVC 2022 paper.

Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Weiyang Liu*, Zeju Qiu*, Yao Feng**, Yuliang Xiu**, Yuxuan Xue**, Longhui Yu**, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black,, Adrian Weller, Bernhard Schölkopf.
ICLR 2024, Vienne
BibTex / Arxiv / Website / Code

We propose BOFT (Orthogonal Butterfly), a general orthogonal finetuning technique with butterfly factorization that effectively adapts foundation models to different tasks such as Vision, NLP, Math QA, and Controllable Generation.

NSF: Neural Surface Fields for Human Modelling from Monocular Depth
Yuxuan Xue*, Bharat Lal Bhatnagar*, Riccardo Marin, Nikolaos Sarafianos, Yuanlu Xu, Gerard Pons-Moll, Tony Tung.
ICCV 2023, Paris
BibTex / Arxiv / Website / Poster / Video (5min) / Code

We propose a new approach to define a neural field on the surface for reconstructing animatable clothed human from monocular depth observation. Our approach directly outputs coherent meshes across different poses at arbitrary resolution.

Controlling Text-to-Image Diffusion by Orthogonal Finetuning
Zeju Qiu*, Weiyang Liu*, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf.
NeurIPS 2023, New Orleans
BibTex / Arxiv / Website / Code

We propose Orthogonal Finetuning (OFT), a fine-tuning approach for adapting text-to-image diffusion models to downstream tasks. OFT can preserve hyperspherical energy to maintain the semantic generation ability of the foundation models.

Event-based Non-Rigid Reconstruction from Contours
Yuxuan Xue, Haolong Li, Stefan Leutenegger, Jörg Stückler.
BMVC 2022, London, Oral, Best Student Paper Award
BibTex / Arxiv / Website / Oral Presentation (11min) / Poster

We propose a new approach for reconstructing fast non-rigid object deformations using measurements from event-based cameras. Our approach estimates object deformation from events at the object contour within a probabilistic optimization (EM) framework.

Robust event detection based on spatio-temporal latent action unit using skeletal information
Hao Xing, Yuxuan Xue, Mingchuan Zhou, Darius Burschka.
IROS 2021, Prague
BibTex / Arxiv

We present a new method for detecting event actions from skeletal information in RGBD videos. The proposed method uses a Gradual Online Dictionary Learning algorithm to cluster and filter skeleton frames. Additionally, the method includes a latent unit temporal structure to better distinguish event actions from similar actions.

Event-based Non-Rigid 3D Tracking

Supervisor: Prof. Dr. Stefan Leutenegger
Advisor: Dr. Jörg Stückler, Haolong Li, M.Sc.
Defended in Jun. 2022
BibTex / VollText

Master Thesis in Robotics, Cognition, Intelligence (RCI) at TUM. Deformable objects tracking using monocular event stream.

A High Precision Lane Following Control Method for an Autonomous Robot

Supervisor: Prof. Dr.-Ing. Markus Lienkamp
Advisor: Jean-Michael Georg
Defended in Jan. 2020
BibTex / VollText

Bachelor thesis in Mechanical Engineering at TUM. High precision Lane Following control algorithm developed for autonomous driving car.

Academic Services
Conference Reviewer: ICCV 2023, CVPR 2024, ECCV 2024, NeurIPS 2024
Journal Reviewer: T-PAMI, SigGraph, SigGraph Asia

   Updated June. 2024