Rui Xu @xrvitd
Ph.D. student, The University of Hong Kong, computer graphics and geometry. ruixu.me Joined May 2020-
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Check out 🌟Vid2Sim: Generalizable, Video-based Reconstruction of Appearance, Geometry & Physics for Mesh-Free Simulation #CVPR2025, from @LingjieLiu1’s lab at UPenn. Congrats to @MorPhLingXD! Vid2Sim aims to achieve system identification by reconstructing geometry, appearance,…
Cool
SuperGaussians: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors @xrvitd, Wenyue Chen, @w080707, @YuanLiu41955461, @totoro97_, Lin Gao, Shiqing Xin, Taku Komura, Xin Li, Wenping Wang arxiv.org/abs/2411.18966
#SIGGRAPHASIA #SIGGRAPHASIA2024 #ACMTOG 🐟 We introduce Collective Behavior Imitation Learning (CBIL), a scalable, self-supervised framework for learning fish schooling behaviors from videos, to be presented at SIGGRAPH ASIA 2024 Tokyo (journal track)🗼! 🦈Reproducing realistic…
Exciting news! 🌟 #SIGGRAPHAsia2025 will be held in #HongKong at #HKCEC! With #SIGGRAPHAsia2024 in Tokyo set for success, we look forward to dynamic innovation in both cities. See you there! 🗓Save the dates! Tokyo: 3-6 Dec'24 @ TIF + Hong Kong: 15-18 Dec'25 @ HKCEC! #HKTB #MEHK
1/ Happy to share our new paper "Surface-Filling Curve Flows via Implicit Medial Axes" at #SIGGRAPH2024! We introduce a fast, robust and user-controllable algorithm to compute surface-filling curves. 🐍✈️ dgp.toronto.edu/projects/surfa…
Our Surf-D is accepted at ECCV 2024 @eccvconf. Codes are released. Surf-D can generate arbitrary typology shapes in high resolution using UDF representation. Project page: yzmblog.github.io/projects/SurfD/ Arxiv: arxiv.org/abs/2311.17050 Codes: github.com/Yzmblog/SurfD #eccv #SurfD
powerful medial axis!
powerful medial axis!
Compact Representation Computation for Shapes—Summary of the Coverage Axis Series The latest work in the Coverage Axis series🐱, Coverage Axis++, has recently been completed and accepted by SGP2024. Here, I provide a summary of our efforts on this problem. In the Coverage Axis…
Watching Prof. Lin Lu’s (Shandong U) keynote at GMP and my day is made! Her team used DeepCurrents from my former students David Palmer+@_DimaSmirnov and collaborators @agrograndma @theAlbertChern to design materials. Exciting to see when tools actually get adopted (and work?!).
Introduce our new work ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models. We study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Project: ruixu.me/html/ComboStoc… Code: github.com/Xrvitd/ComboSt…
🔥Check out our SIGGRAPH 2024 paper: 𝐏𝐚𝐫𝐭𝟏𝟐𝟑: 𝐏𝐚𝐫𝐭-𝐚𝐰𝐚𝐫𝐞 𝟑𝐃 𝐑𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐟𝐫𝐨𝐦 𝐚 𝐒𝐢𝐧𝐠𝐥𝐞-𝐯𝐢𝐞𝐰 𝐈𝐦𝐚𝐠𝐞. Diffusion models have advanced single-view reconstruction, but current methods lack 𝗣𝗮𝗿𝘁-𝗯𝗮𝘀𝗲𝗱 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀,…
congrats!
Very excited to share our work on SIGGRAPH 2024: CWF: Consolidating Weak Features in High-quality Mesh Simplification. Proj: ruixu.me/html/CWF/index… Code: github.com/Xrvitd/CWF (some star pls) Paper:arxiv.org/abs/2404.15661 YouTube:youtu.be/QpTY-cHJJRU
Look to the sky and find new discoveries in #CG ☁️ We sat down with one of the contributors behind the #SIGGRAPH2023 Technical Paper “Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-number Field" to shed light on their exciting research that…
🔥Yes! You can achieve REAL-TIME text-to-motion generation using a simulated humanoid to perform various skills! This feat is realized through the integration of PHC and EMDM. 💬This combination addresses two pivotal challenges in human motion synthesis: ensuring physical…
🔥Yes! You can achieve REAL-TIME text-to-motion generation using a simulated humanoid to perform various skills! This feat is realized through the integration of PHC and EMDM. 💬This combination addresses two pivotal challenges in human motion synthesis: ensuring physical…
It feels so great to win the best paper award at #SIGGRAPH2023! Congratulations to my collaborators @frankzydou @ninnnnnglala !
Sharing our work TORE - We significantly reduce the tokens of Transformers for human mesh recovery, achieving superior efficiency with competitive accuracy!
Sharing our work TORE - We significantly reduce the tokens of Transformers for human mesh recovery, achieving superior efficiency with competitive accuracy!

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