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🚀A Simple Baseline for Streaming Video Understanding🚀 #SimpleStream reveals a simple baseline of recent N-frame sliding window beats SOTA memory-based methods on standard streaming video benchmarks lmms-lab - Project: - Code:

Ziwei Liu

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36,054 görüntüleme • 3 ay önce •via X (Twitter)

Eğitim Haberler & Politika Bilim & Teknoloji #SimpleStream

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MrNeRF

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Sudo su

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This happened at a public school in Southern California, students streaming to the altar to give their lives to Jesus! 😭 Watch it for yourself… Yesterday, I posted a full video sharing the vision to reach every school possible in 2026. It would be an honor to have you partner with us. How? Simple. We are believing for another 100 monthly financial supporters as we preach the gospel, disciple them and give students bibles on campus (yes it’s true) Would you partner with us on this initiative to save our generation?

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18,789 görüntüleme • 7 ay önce

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You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

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Excited that our paper StreamdiffusionV2 received the Best Research Paper Award at #MLSys26! 🚀Video generation is quickly moving from demos to production-facing workloads. It is no longer a turn-based pipeline but should be a streaming pipeline to interact with users. 📖Our project page: and paper: 👂Come join the talk if you are interested in streaming video generation. Our talk will be at the Research Track Oral Presentation: Best Paper Session on Tue 8:45AM at #MLSys26 , I will talk about how we attacked the efficiency and quality challenges. Hope to see you there! ❤️Huge thanks to all authors! This work would not have been possible without the incredible effort from the entire team. Big shout out to Tianrui Feng, Zhi Li, Shuo Yang , Haocheng Xi, Muyang Li , lllyasviel , Xiuyu Li , Keting Yang, Kelly Peng, Song Han , Maneesh Agrawala, Kurt Keutzer , and あき先生(Aki)

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World modeling and imitation learning have largely been considered two disparate worlds. In our recent work, Unified World Models, just accepted to #RSS2025, Chuning Zhu provides a dead-simple unifying solution: just train a joint diffusion model over actions and future states, but with *decoupled* diffusion time steps across these modalities. Manipulating these decoupled time steps then allows for marginalization or conditioning on actions or states; a single model can serve as a policy, forward dynamics model, video prediction model, or inverse dynamics model by simply setting diffusion timesteps carefully. The resulting model can leverage video datasets along with robot training data much more effectively, and shows improved robustness, generalization, and flexibility. This is exciting because it is frustratingly simple, scalable, and shows strong improvement on real-world robotics problems. Please refer to Chuning Zhu 's excellent thread for more details! More details/code can be found on our website and in the paper -

World modeling and imitation learning have largely been considered two disparate worlds. In our recent work, Unified World Models, just accepted to #RSS2025, Chuning Zhu provides a dead-simple unifying solution: just train a joint diffusion model over actions and future states, but with decoupled diffusion time steps across these modalities. Manipulating these decoupled time steps then allows for marginalization or conditioning on actions or states; a single model can serve as a policy, forward dynamics model, video prediction model, or inverse dynamics model by simply setting diffusion timesteps carefully. The resulting model can leverage video datasets along with robot training data much more effectively, and shows improved robustness, generalization, and flexibility. This is exciting because it is frustratingly simple, scalable, and shows strong improvement on real-world robotics problems. Please refer to Chuning Zhu 's excellent thread for more details! More details/code can be found on our website and in the paper -

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Luxxcoin

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TEAM CAPRICE OFFICIAL

64,180 görüntüleme • 1 ay önce

🚀 Excited to share our #ICLR2025 work on planning with neural dynamics models! While our lab has developed diverse neural dynamics models for manipulating rigid, deformable, and granular objects, having the model alone doesn’t solve the problem—planning with it remains a challenge. 💡 Enter BaB-ND, led by Keyi and Jiangwei! We propose a scalable, GPU-accelerated branch-and-bound algorithm, inspired by neural network verification, to enable effective planning for diverse objects modeled with neural dynamics. 🔗 Project page (open-source + detailed docs!): 🎥 Watch the video to see T being pushed around obstacles, and check out Keyi’s thread for more details!

🚀 Excited to share our #ICLR2025 work on planning with neural dynamics models! While our lab has developed diverse neural dynamics models for manipulating rigid, deformable, and granular objects, having the model alone doesn’t solve the problem—planning with it remains a challenge. 💡 Enter BaB-ND, led by Keyi and Jiangwei! We propose a scalable, GPU-accelerated branch-and-bound algorithm, inspired by neural network verification, to enable effective planning for diverse objects modeled with neural dynamics. 🔗 Project page (open-source + detailed docs!): 🎥 Watch the video to see T being pushed around obstacles, and check out Keyi’s thread for more details!

Yunzhu Li

10,561 görüntüleme • 1 yıl önce

Power Unrivalled 💪 Our infrastructure speaks for itself. From ASIC miners to GPUs and Nodes, every piece of our hardware is real, operational, and we have full and complete ownership. With multiple facilities worldwide, we’re setting the standard others can only aspire to. Transparency for Hash AI is made simple as a result of genuine expertise and dedication to building profitable, scalable, and future-proof infrastructure. The video below showcases our GPUs actively mining in Ethiopia, one of our many operation centres. This is the sound of progress and power, built on a foundation of hard work and commitment to excellence. #BTC #DOGE #LTC

Power Unrivalled 💪 Our infrastructure speaks for itself. From ASIC miners to GPUs and Nodes, every piece of our hardware is real, operational, and we have full and complete ownership. With multiple facilities worldwide, we’re setting the standard others can only aspire to. Transparency for Hash AI is made simple as a result of genuine expertise and dedication to building profitable, scalable, and future-proof infrastructure. The video below showcases our GPUs actively mining in Ethiopia, one of our many operation centres. This is the sound of progress and power, built on a foundation of hard work and commitment to excellence. #BTC #DOGE #LTC

Hash AI

39,913 görüntüleme • 1 yıl önce