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_config.yml

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name : "Cecilia Curreli"
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avatar : "/assets/images/profile-photo.jpg"
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bio : >-
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PhD student @ <a href="https://cvg.cit.tum.de">TUM CVG</a><br>
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PhD student @ <a href="https://cvg.cit.tum.de">TUM CVG</a><br> @ <a href="https://mcml.ai/">MCML</a><br>
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cecilia.curreli@tum.de
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links:
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- label: "CVG Page"
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icon: "fa-solid fa-fw fa-flask"
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url: "https://cvg.cit.tum.de/members/curreli"
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- label: "LinkedIn"
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icon: "fa-brands fa-fw fa-linkedin"
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url: "https://de.linkedin.com/in/ceciliacurreli"
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- label: "Google Scholar"
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icon: "fa-brands fa-fw fa-google-scholar"
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url: "https://scholar.google.com/citations?user=Y6wCaD4AAAAJ&hl=en"
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- label: "CVG Lab"
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icon: "fa-solid fa-fw fa-flask"
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url: "https://cvg.cit.tum.de/members/curreli"
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- label: "X (Twitter)"
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# icon: "fa-brands fa-fw fa-x-twitter"
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# url: "https://twitter.com/dominikmuhle"
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# - label: "GitHub"
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# icon: "fa-brands fa-fw fa-github"
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# url: "https://github.com/dominikmuhle"
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icon: "fa-brands fa-fw fa-x-twitter"
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url: "https://x.com/CeciCurrl"
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- label: "GitHub"
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icon: "fa-brands fa-fw fa-github"
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url: "https://github.com/Ceveloper"
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- label: "Bluesky"
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icon: "fa-brands fa-fw fa-bluesky"
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url: "https://bsky.app/profile/ceciliacurreli.bsky.social"
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# ==================================================== COLLECTIONS ====================================================
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collections:
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type: posts
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values:
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layout: single
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author_profile: false
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# author_profile: true
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# permalink: /:name/

_layouts/welcome.html

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{{ content }}
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<p align="justify">
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I am pursuing my PhD with Prof. Cremers at the <a href="https://cvg.cit.tum.de">Computer Vision Group</a> of the Technical University of Munich (TUM) - Check out the awesome work of our <a href="https://cvg.cit.tum.de/publications">group</a>!
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I am pursuing my PhD at the Technical University of Munich with Prof. Cremers - Check out the awesome work of the <a href="https://cvg.cit.tum.de/publications">Computer Vision Group</a>!
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I am a junior member of the <a href="https://mcml.ai/">Munich Center of Machine Learning</a>, one of six national AI Competence Centers in Germany.
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<br>
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My <a href="publications">current research</a> focuses on generative AI, in particular diffusion for 4D computer vision tasks and 3D human motion prediction and generation.
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<br>
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In 2022 I joined <a href="https://www.satoh-lab.nii.ac.jp/">Satoh's Lab</a> at the University of Tokyo (UTokyo) and National Intitute of Informatics in Tokyo (NII) as a research intern investigating probabilistic aspects and uncertainty in conditioned human motion generation under the guidance of <a href="https://research.nii.ac.jp/~satoh/index.html">Prof. Shin'ichi Satoh</a> and <a href="https://agirbau.github.io/">Dr. Andreu Girbau</a>.
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<br>
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I completed my Master's thesis with <a href="https://www.niessnerlab.org/members/matthias_niessner/profile.html">Prof. Nießner</a> at Nießner Lab on "Probabilistic Human Motion Prediction with Scene Understanding and Occlusion Handling" completing my degree in computer science (Robotics, Cognition, Intelligence) at TU Munich.
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<br>
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I obtained my Bachelor's degree in engineering science at TU Munich after completing a thesis on "Image Retrieval Under Challenging Weather and Lighting Conditions for Place Localization in Autonomous Driving" with <a href="https://research.nvidia.com/labs/dvl/author/laura-leal-taixe/">Prof. Leal Taixè </a> and <a href="https://scholar.google.com/citations?user=d5DqmRoAAAAJ">Qunjie Zhou</a>.
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My <a href="publications">research</a> focuses on generative AI, in particular diffusion for 4D computer vision and 3D human motion prediction and generation.
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In 2022 I joined <a href="https://www.satoh-lab.nii.ac.jp/">Satoh's Lab</a> at the University of Tokyo and National Intitute of Informatics in Tokyo as a research intern under the guidance of Prof. Shin'ichi Satoh and Dr. Andreu Girbau.
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I completed my Master's thesis with Prof. Nießner and my my Bachelor's degree with Prof. Leal Taixè and Qunjie Zhou, both at TUM.
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</p>
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<div class="hr">

_publications/skeletondiffusion/content.html

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<div class="hero-body">
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<div class="column is-five-fifths">
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<img width="100%" src="./assets/teaser_image.png" />
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<br><br>

_publications/skeletondiffusion/index.md

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affiliations: "1,2"
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affiliations:
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- name: tum
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length: short
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length: long
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- name: mcml
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length: short
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length: long
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description: "SkeletonDiffusion is a novel nonisotropic diffusion approach for 3D Human Motion Prediction, and the first computer vision method to show that nonisotropic diffusion leads to unequivocally better performance without computational drawbacks for a structured task. We generate diverse and realistic motions achieving state-of-the-art performance on the Human3.6M and AMASS datasets."
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citation: '@inproceedings{curreli2025nonisotropic,
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title={Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction},
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author={Curreli, Cecilia and Muhle, Dominik and Saroha, Abhishek and Ye, Zhenzhang and Marin, Riccardo and Cremers, Daniel},
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author={Cecilia Curreli and Dominik Muhle and Abhishek Saroha and Zhenzhang Ye and Riccardo Marin and Daniel Cremers},
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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year={2025}
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}'
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_publications/zdyss/content.html

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<!-- <section class="hero teaser">
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<video width="100%" autoplay muted loop>
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<source src="./assets/header_vid.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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<p class="content has-text-justified">
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<i><b> Visual Odometry on KITTI. </b> Input image (top left), color-coded dense covariances (bottom left) and resulting trajectory (right). Color coding of the covariances is given by hue (size), orientation (color), and saturation (anisotropy). First sequence uses our supervised covariances, second sequence uses our unsupervised covariances.</i>
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</section> -->
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<img width="140%" src="./assets/teaser.png" />
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<br><br>
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<!-- a) We propose a symetric extension of the <a href="https://arxiv.org/abs/2204.02256" target="_blank">Probabiltistic Normal Epipolar Constraint</a>() (PNEC) to more accurately model the geometry of relative pose estimation with uncertain feature positions.
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<br>
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b) We propose a learning strategy to minimize the relative pose error by learning feature position uncertainty through differentiable nonlinear least squares (DNLS). This learning strategy can be combined with any feature extraction algorithm. We evaluate our learning framework with synthetic experiments and on real-world data in a visual odometry setting. We show that our framework is able to generalize to different feature extraction algorithms such as SuperPoint and feature tracking approaches. -->
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<div class="container">
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<h2 class="title is-2">Coming Soon</h2>
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</p>
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</section>

_publications/zdyss/index.md

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permalink: /publications/zdyss/
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# external_link: https://abhi-rf.github.io/publications/gss/
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date: 2024_04_11 # determines sorting just take the date of the first publication as YYYY_MM_DD
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# image: /assets/qualitative.gif
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image: /assets/teaser_zdyss.png
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# image_mouseover: assets/knowledge_distillation.png
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title: "ZDySS – Zero-Shot Dynamic Scene Stylization using Gaussian Splatting"
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links:
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# - name: Project Page
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# link: https://keonhee-han.github.io/publications/kdbts/
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- name: Project Page
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link: /publications/zdyss/
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- name: Paper
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link: https://arxiv.org/abs/2501.03875
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style: "bi bi-file-earmark-richtext"
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# acknowledgements: 'This work was supported by the ERC Advanced Grant SIMULACRON, by the Munich Center for Machine Learning, and by the German Federal Ministry of Transport and Digital Infrastructure (BMDV) under grant 19F2251F for the ADAM project.'
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{% include_relative content.html %}

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