Yueh-Cheng Liu
@liuyuehcheng
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PhD Student at @TU_Muenchen 3D AI Lab
Taiwan
Joined May 2020
See you today at #ICCV2025 this afternoon 14:30-16:30 at poster 301!
We will present QuickSplat at #ICCV2025! ๐ Data-driven 2DGS initialization and densification makes 3D surface reconstruction fast & accurate! ๐ Projcet: https://t.co/SKJ7XRWTVt Arxiv:
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Our new benchmark iPhone NVS. Super interesting when the data captures are not โperfectโ.
๐ข๐ข๐ขWe've released the ScanNet++ Novel View Synthesis Benchmark for iPhone data! ๐ฅณ Test your models on RGBD video featuring real-world challenges like exposure changes & motion blur! Download the newest iPhone NVS test split and submit your results! โฌ๏ธ https://t.co/hLnFwifvTL
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Can we use video diffusion to generate 3D scenes? ๐๐จ๐ซ๐ฅ๐๐๐ฑ๐ฉ๐ฅ๐จ๐ซ๐๐ซ (#SIGGRAPHAsia25) creates fully-navigable scenes via autoregressive video generation. Text input -> 3DGS scene output & interactive rendering! ๐ https://t.co/HBdrmU4Oqq ๐ฝ๏ธ https://t.co/AQr0p4uWBZ
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Excited to join @uvadatascience as an Assistant Professor! Deeply grateful to my advisors @angelaqdai, Maks Ovsjanikov, Hongbo Fu, and Chiew-Lan Tai for their unwavering support. ๐ขWe are recruiting PhD students and postdocs to work on #SpatialAI. Flyer below with details!
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We will present QuickSplat at #ICCV2025! ๐ Data-driven 2DGS initialization and densification makes 3D surface reconstruction fast & accurate! ๐ Projcet: https://t.co/SKJ7XRWTVt Arxiv:
arxiv.org
Surface reconstruction is fundamental to computer vision and graphics, enabling applications in 3D modeling, mixed reality, robotics, and more. Existing approaches based on volumetric rendering...
๐ข QuickSplat: Fast 3D Surface Reconstruction via Learned Gaussian Initialization @liuyuehcheng learns 2DGS initialization, densification, and optimization priors from ScanNet++ => fast & accurate reconstruction! Project: https://t.co/mDgQxmhqkF
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Check out our #ICCV2025 work on functional 3d scan editing, learning to optimize, multi-level 3d captioning, interactive mesh editing, audio-driven avatars, & shape matching! Congrats @ElBoudjogh24002, @liuyuehcheng, @chandan__yes, @hcxrli, @shivangi2201, Emery for amazing work!
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Seven papers accepted at #ICCV2025! Exciting topics: lots of generative AI using transformers, diffusion, 3DGS, etc. focusing on image synthesis, geometry generation, avatars, and much more - check it out! So proud of everyone involved - let's go๐๐๐ https://t.co/Rd4vDGiG5p
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Check out the ScanNet++ workshop! Iโll be there tomorrow!
Check out our ScanNet++ workshop @CVPR June 12 in 211 from 8:50am! Exciting keynotes on sota NVS & 3D understanding from Andrea Vedaldi @Oxford_VGG, @CordeliaSchmid, @GordonWetzstein, @K_S_Schwarz, @QianqianWang5, and leading methods on the benchmark! https://t.co/TvURvNYoW3
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๐ขCode Release of Pixel3DMM ๐ข Looking for a robust and accurate face tracker? Our state-of-the-art tracker handles challenging in-the-wild settings, such as extreme lighting conditions, fast movements, and occlusions. ๐จโ๐ป https://t.co/ynPtiaDvLF ๐ https://t.co/2nsXxUKlK9
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Submit your NVS sota method to the challenge!
๐Join the ๐๐๐๐ง๐๐๐ญ++ ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐ @CVPR 2025! Think your method can handle large-scale 3D scenes? Put it to the test: https://t.co/TvURvNYoW3 Updates: โ
Preprocessed, undistorted DSLR images โ
3DGS demo: https://t.co/8CP9KI3zLA by @liuyuehcheng, @chandan__yes
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๐ข ScanNet++ v2 Benchmark Release! ๐ Test your state-of-the-art models on: ๐น Novel View Synthesis ๐ธโก๏ธ๐ผ๏ธ ๐น 3D Semantic & Instance Segmentation ๐ค๐๐ถ๏ธ Shoutout to @chandan__yes and @liuyuehcheng for their incredible work๐ ๐Check it out: https://t.co/SKCGM23hA0
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๐ขMeshArt: Generating Articulated Meshes with Structure-guided Transformers @DaoyiGao generates articulated meshes with a hierarchical transformer, modeling articulation-aware structures that guide mesh synthesis. w/ @yawarnihal @craigleili Project: https://t.co/aZPVyn8kQd
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Super happy to present our #NeurIPS paper ๐๐จ๐ก๐๐ซ๐๐ง๐ญ ๐๐ ๐๐๐๐ง๐ ๐๐ข๐๐๐ฎ๐ฌ๐ข๐จ๐ง ๐
๐ซ๐จ๐ฆ ๐ ๐๐ข๐ง๐ ๐ฅ๐ ๐๐๐ ๐๐ฆ๐๐ ๐ in Vancouver. Come to our poster #2804 on Wednesday 11am - 2pm in East Exhibit Hall A-C and say hi if you want to learn more about 3D Scene
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Better optimizer for 3DGS!
(1/2) How to accelerate the reconstruction of 3D Gaussian Splatting? 3DGS-LM replaces the commonly used ADAM optimizer with a tailored Levenberg-Marquardt (LM). => We are ๐๐% ๐๐๐ฌ๐ญ๐๐ซ ๐ญ๐ก๐๐ง ๐๐๐๐ for the same quality. https://t.co/F4uB4DpJGt
https://t.co/Ju0Y2VxH7z
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How can we generate high-fidelity, complex 3D scenes? @QTDSMQ's LT3SD decomposes 3D scenes into latent tree representations, with diffusion on the latent trees enabling seamless infinite 3D scene synthesis! w/ @craigleili, @MattNiessner
https://t.co/wv9bIhkkYi
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CAD retrieval with Diffusion!
Excited to present DiffCAD coming to #SIGGRAPH2024! @DaoyiGao introduces the first probabilistic single-view CAD retrieval & alignment. We train only on synthetic -> generalize robustly to real images! Check out the code: https://t.co/hBCoN0Hx3w w/@david_roz_, @StefanLeuteneg1
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Excited to present GenZI at #CVPR2024! @craigleili introduces GenZI, the first zero-shot approach to creating realistic 3D human-scene interactions by leveraging interaction priors from large VLMs. Code and data on our website! https://t.co/hUhMgUoU70
https://t.co/rnn1G5HOuu
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(1/2) LightIt: Illumination Modeling and Control for Diffusion Models! #CVPR2024 We facilitate lighting control for novel image generation from text prompts. We can also edit lighting for a given input image. Video: https://t.co/6aJU4Go5b3 Project: https://t.co/stjDU8TEOa
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Check out @DaoyiGao's DiffCAD - introducing probabilistic CAD retrieval and alignment to an RGB image. We captures ambiguities in depth/scale, inexact CAD matches, and don't require any training on real data! https://t.co/eFZgP9JP4j
https://t.co/giMJl6R9Kl
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(1/2) Check out ๐๐๐ฎ๐ฌ๐ฌ๐ข๐๐ง๐๐ฏ๐๐ญ๐๐ซ๐ฌ: Photorealistic Head Avatars with Rigged 3D Gaussians! We create photorealistic head avatars by animating 3D Gaussians on a parametric face model - edited and rendered in *real-time*! https://t.co/R90VnWJB9Y
https://t.co/Gv5gED01SG
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