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
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[2024-10] Our Paper Gen-3Diffusion is available on Arxiv.
[2024-09] Our Paper Human 3Diffusion is accepted to NeurIPS 2024.
[2024-07] Awarded $5000 from OpenAI Research Access Program .
[2024-02] Our Paper E-LnR is accepted to IJCV (Vol.132).
[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.
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Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy
Yuxuan Xue,
Xianghui Xie,
Riccardo Marin,
Gerard Pons-Moll
Pre-print
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Arxiv
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Website
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Code
We extend the idea of Human 3Diffusion to general objects. Our Gen-3Diffusion reconstructs high-fidelity 3D representation from single RGB Image within 22 seconds and 11 GB GPU memory, which allows an efficient large-scale 3D generation.
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Human 3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models
Yuxuan Xue,
Xianghui Xie,
Riccardo Marin,
Gerard Pons-Moll
NeurIPS 2024, Vancouver
BibTex
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Arxiv
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Website
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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.
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E-LnR: Event-Based Non-rigid Reconstruction of Low-Rank Parametrized Deformations from Contours
Yuxuan Xue,
Haolong Li,
Stefan Leutenegger,
Jörg Stückler.
IJCV Volume 132, pages 2943–2961
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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.
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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
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Arxiv
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Website
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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.
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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
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Arxiv
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Poster
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Video (5min)
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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.
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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
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Arxiv
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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.
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Event-based Non-Rigid Reconstruction from Contours
Yuxuan Xue,
Haolong Li,
Stefan Leutenegger,
Jörg Stückler.
BMVC 2022, London, Oral, Best Student Paper Award
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Arxiv
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Website
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Oral Presentation (11min)
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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.
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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
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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.
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Academic Services
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Conference Reviewer: ICCV 2023, CVPR 2024, ECCV 2024, NeurIPS 2024, 3DV 2025, ICLR 2025
Journal Reviewer: T-PAMI, SigGraph, SigGraph Asia
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