Cheng Wan
@jornywan
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Ph.D student @Cornell | Prev. @GeorgiaTech | Computer Vision, AI for Medicine
New York, USA
Joined September 2022
What a crazy model……
Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: . The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n
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RT @theworldlabs: We’ve been busy building an AI system to generate 3D worlds from a single image. Check out some early results on our site…
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@IEEEBHI This is my last work at #GeorgiaTech! 🎓 We developed a novel Encoder-Decoder framework to advance sleep stage classification and effectively link sleep stage features to OSA severity, our work paves the way for precise, automated diagnostics of sleep disorders. #SleepApnea
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RT @_sakshams_: We introduce LiFT, an easy to train, lightweight, and efficient feature upsampler to get dense ViT features without the nee…
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@pliang279 @medialab @MITEECS @MIT @MIT_CSAIL @MITEngineering @mldcmu @LTIatCMU @SCSatCMU Congrats Prof. Paul!
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RT @pliang279: 📣 I'm thrilled to share that I’ll be joining MIT as an assistant professor this fall, joint between @medialab & @MITEECS. M…
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RT @taziku_co: 【2枚の写真を用いた動画生成】 異なる写真を同時にインプットしてプロンプト使用して制御。物理的な制約を超えて一つの動画につなげている。 #動画生成AI #AI
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RT @ChunyuanDeng: 🔎 Uncover the spectrum of data contamination in our latest #ACL2024 Findings paper! Our paper dives deep into the impact,…
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RT @sainingxie: Introducing Cambrian-1, a fully open project from our group at NYU. The world doesn't need another MLLM to rival GPT-4V. Ca…
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RT @LumaLabsAI: #LumaDreamMachine is product-marketing powerhouse that let’s you create entire commercials & mockups in minutes rather than…
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