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Minghua Liu

@MinghuaLiu_ • 2,473 subscribers

Founding member @sudo_robotics. Embodied AI, 3D vision. | ex: @nvidia @Qualcomm @Waymo @Adobe @ucsd_cse @Tsinghua_Uni

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In Hong Kong until Thursday! 🇭🇰✨ I’ll be presenting three works at SIGGRAPH Asia: PartUV (Monday afternoon) and FreeArt3D & LARM (Thursday morning), all in meeting room S421! Let me know if you’re nearby! Can’t wait to catch up, chat, and hang out! 🌃🧋🍖🦆🍻🍜
0:20
MinghuaLiu_'s profile picture

In Hong Kong until Thursday! 🇭🇰✨ I’ll be presenting three works at SIGGRAPH Asia: PartUV (Monday afternoon) and FreeArt3D & LARM (Thursday morning), all in meeting room S421! Let me know if you’re nearby! Can’t wait to catch up, chat, and hang out! 🌃🧋🍖🦆🍻🍜

Minghua Liu

14,231 Aufrufe • vor 10 Monaten

🚀Excited to release PartField—a feedforward model that learns part-based feature fields for 3D shapes! It enables lightning-fast⚡️, robust, open-world hierarchical 3D part seg and unlocks cross-shape applications like co-seg and correspondence! 🔗 1/n
2:21
MinghuaLiu_'s profile picture

🚀Excited to release PartField—a feedforward model that learns part-based feature fields for 3D shapes! It enables lightning-fast⚡️, robust, open-world hierarchical 3D part seg and unlocks cross-shape applications like co-seg and correspondence! 🔗 1/n

Minghua Liu

17,424 Aufrufe • vor 1 Jahr

Want high-quality 3D meshes with sharp geometric details? Try our newly released MeshFormer! It only takes 8 GPUs for two days of training, outperforming state-of-the-art models that use over a hundred GPUs! With 3D-native input guidance, representations, supervision, and post-processing, we significantly improve the training efficiency and geometric quality of feed-forward reconstruction models! Project page: Chong Zeng
0:21
MinghuaLiu_'s profile picture

Want high-quality 3D meshes with sharp geometric details? Try our newly released MeshFormer! It only takes 8 GPUs for two days of training, outperforming state-of-the-art models that use over a hundred GPUs! With 3D-native input guidance, representations, supervision, and post-processing, we significantly improve the training efficiency and geometric quality of feed-forward reconstruction models! Project page: Chong Zeng

Minghua Liu

16,374 Aufrufe • vor 2 Jahren

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