Hezhen Hu @ CVPR2026

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Hezhen Hu @ CVPR2026

Hezhen Hu @ CVPR2026

@AlexHu0212

Postdoc at UT-Austin @VITAGroupUT @UTAustin, Previous Intern @MSFTResearch | 3DV, Digital Human, Sign Langauge Open to collaboration!

Austin, TX Katılım Ekim 2023
124 Takip Edilen71 Takipçiler
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Hezhen Hu @ CVPR2026
Hezhen Hu @ CVPR2026@AlexHu0212·
AI3DCC Workshop @ICCVConference We are excited to announce that the 3rd International Workshop on AI for 3D Content Creation (AI3DCC) will take place on October 20th, 2025 (8:00–12:30) in conjunction with ICCV 2025, Honolulu. ✨This year, we are honored to have five distinguished keynote speakers from both academia and industry. 🖼️We will also host an interactive poster session, offering students and researchers the opportunity to present their latest work and engage with the community. Self-nominations for posters are welcome. forms.gle/P3YUEVFWYV7xFU… 📄 Learn more at: ai3dcc.github.io
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Tencent HY
Tencent HY@TencentHunyuan·
One static model does not fit all😭 We just dropped our latest work: Functional Neural Memory. Instead of static models, we generate custom "parameters" for every single input. ✅Prompt your model anytime ✅Instant personalization ✅Better instruction following ✅Flexible & dynamic memory (w/o memory bank✌️) (🧵1/6)
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Zhiwen(Aaron) Fan
Zhiwen(Aaron) Fan@zhiwen_fan_·
What happens when VLMs meet 3D foundation models? See VLM-3R (CVPR 2026). VLM-3R links a vision-language model (e.g., Qwen) with 3D geometric foundation models (e.g., CUT3R) at metric scale. Given an uncalibrated video, it moves beyond pixels to perceive and reason in 3D space. Code (open source): vlm-3r.github.io
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Hezhen Hu @ CVPR2026
Hezhen Hu @ CVPR2026@AlexHu0212·
Join the 1st Workshop on Generative AI for Sign Language (GenSign) at CVPR 2026 @CVPR. 🚀 Paper submissions are NOW OPEN! We welcome papers on sign language processing, human-centric generative models, datasets/benchmarks, and ethics. 📌 Proceedings track DDL: Mar 14, 2026 (AoE) 📌 Non-proceedings track DDL: Apr 4, 2026 (AoE) 🌐 genai4sl.github.io ✉️ gensign.workshop@gmail.com #CVPR2026 #Sign_language #Human_centric #Benchmarks #Workshop
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Georgios Pavlakos
Georgios Pavlakos@geopavlakos·
We are organizing the 3rd edition of the workshop on AI for 3D Content Creation today at #ICCV2025! Come join us for a great lineup of speakers! Huge thanks to @AlexHu0212 for running the show! Website: ai3dcc.github.io
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Hezhen Hu @ CVPR2026
Hezhen Hu @ CVPR2026@AlexHu0212·
🚀Call for Poster Presentation: AI for 3D Content Creation @ICCVConference ! Want to showcase your research to the community? Nominate yourself for a poster presentation (t.co/bTTshgkSe7) Topics of interest include, but are not limited to: 3D representations, modeling of humans, objects, and scenes, interaction modeling, applications, and etc. 📔Learn more: ai3dcc.github.io #ICCV2025 #AI3DCC #3DVision
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Hezhen Hu @ CVPR2026
Hezhen Hu @ CVPR2026@AlexHu0212·
AI3DCC Workshop @ICCVConference We are excited to announce that the 3rd International Workshop on AI for 3D Content Creation (AI3DCC) will take place on October 20th, 2025 (8:00–12:30) in conjunction with ICCV 2025, Honolulu. ✨This year, we are honored to have five distinguished keynote speakers from both academia and industry. 🖼️We will also host an interactive poster session, offering students and researchers the opportunity to present their latest work and engage with the community. Self-nominations for posters are welcome. forms.gle/P3YUEVFWYV7xFU… 📄 Learn more at: ai3dcc.github.io
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Zhiwen(Aaron) Fan
Zhiwen(Aaron) Fan@zhiwen_fan_·
We present VLM-3R: a Vision-Language Model capable of 3D spatial reasoning from monocular video, grounding visual cues, geometry, and camera motion. ✅ No depth sensor ✅ No pre-built 3D maps ✅ End-to-end spatial + temporal reasoning 🔗 Code & benchmark: vlm-3r.github.io #VLM #3DVision #LLMs
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Victor.Kai Wang
Victor.Kai Wang@VictorKaiWang1·
Generating ~200 million parameters in just minutes! 🥳 Excited to share our work with @MTDovent , @heisejiasuo96 , and @YangYou1991: 'Recurrent Diffusion for Large-Scale Parameter Generation' (RPG for short). Example: Obtain customized models using prompts (see below). (🧵1/8)
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