Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

🚀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

13,396 subscribers

36,171 просмотров • 5 месяцев назад •via X (Twitter)

Образование Новости и политика Наука и технологии #SimpleStream

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Announcing Bespoke-MiniChart-7B, a new SOTA in chart understanding for models of comparable size on seven benchmarks, on par with Gemini-1.5-Pro and Claude-3.5! 🚀 Beyond its real-world applications, chart understanding is a good challenging problem for VLMs, since it requires both mathematical as well as visual reasoning. 1/n🧵

Announcing Bespoke-MiniChart-7B, a new SOTA in chart understanding for models of comparable size on seven benchmarks, on par with Gemini-1.5-Pro and Claude-3.5! 🚀 Beyond its real-world applications, chart understanding is a good challenging problem for VLMs, since it requires both mathematical as well as visual reasoning. 1/n🧵

Bespoke Labs

19,871 просмотров • 1 год назад

Unlimited-OCR is a 3B parameter model that parses entire 100-page PDFs in one shot — no page chunking, no lost context. +⁠32K context window, reads the whole document in a single pass +⁠93% on standard parsing benchmarks, +6 over baseline +⁠Under 0.11 error rate past page 40 — where every other OCR tool falls apart ⁠+Multilingual out of the box, runs locally via Transformers, Ollama, llama.cpp, Docker +⁠Textract, Google Vision, and Azure Doc Intelligence charge $1.50–$15 per 1,000 pages 1.9M downloads on HuggingFace and most people have never heard of it. This runs on your machine. For free. Forever. Model link:

Unlimited-OCR is a 3B parameter model that parses entire 100-page PDFs in one shot — no page chunking, no lost context. +⁠32K context window, reads the whole document in a single pass +⁠93% on standard parsing benchmarks, +6 over baseline +⁠Under 0.11 error rate past page 40 — where every other OCR tool falls apart ⁠+Multilingual out of the box, runs locally via Transformers, Ollama, llama.cpp, Docker +⁠Textract, Google Vision, and Azure Doc Intelligence charge $1.50–$15 per 1,000 pages 1.9M downloads on HuggingFace and most people have never heard of it. This runs on your machine. For free. Forever. Model link:

0xMarioNawfal

84,468 просмотров • 1 месяц назад

(1/n) 🚀 With FastVideo, you can now generate a 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licenses

(1/n) 🚀 With FastVideo, you can now generate a 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licenses

Hao AI Lab

79,140 просмотров • 1 год назад

Kling 2.6 Motion Capture is so good. It's a huge leap forward. We've been able to do motion capture with AI for a while, but the quality bump finally made this approach useful. Here are the steps: - Get a reference video - record it or get a stock video - Grab the start frame of that video - Edit it using Nano Banana Pro. Ask to replace character and background. - Select the Kling 2.6 Motion Capture model in the video generator on Freepik (now Magnific) - Upload reference video + set the edited start frame - Video prompt can be simple e.g. "Gandalf dancing"

Kling 2.6 Motion Capture is so good. It's a huge leap forward. We've been able to do motion capture with AI for a while, but the quality bump finally made this approach useful. Here are the steps: - Get a reference video - record it or get a stock video - Grab the start frame of that video - Edit it using Nano Banana Pro. Ask to replace character and background. - Select the Kling 2.6 Motion Capture model in the video generator on Freepik (now Magnific) - Upload reference video + set the edited start frame - Video prompt can be simple e.g. "Gandalf dancing"

Martin LeBlanc

35,274 просмотров • 8 месяцев назад

Do you use Runway Gen-2 to make AI videos? This is for you! I built a simple tool to help "extend" Runway videos beyond the 4 second clip limit. Just choose a video, and the tool will give you the final frame -- so that you can drop this back into Runway, and generate another AI video based on it. Let me know if this is useful to you! Link is at bottom of the video and it's live to try right now.

Do you use Runway Gen-2 to make AI videos? This is for you! I built a simple tool to help "extend" Runway videos beyond the 4 second clip limit. Just choose a video, and the tool will give you the final frame -- so that you can drop this back into Runway, and generate another AI video based on it. Let me know if this is useful to you! Link is at bottom of the video and it's live to try right now.

Benjamin De Kraker

68,384 просмотров • 3 лет назад

🎥 Video generation is hitting the memory wall. As videos get longer, the KV cache quietly explodes — and long-horizon consistency starts to break. We built Quant VideoGen: a training-free KV cache compression method for auto-regressive video diffusion. Instead of storing every KV in high precision, QVG exploits video’s spatiotemporal redundancy with semantic-aware smoothing + progressive residual quantization. 🚀 Up to 7× KV memory reduction ⚡ <4% overhead ✅ Strong long-video quality 🕹️ Deploy HYWorldPlay on your own RTX 5090 locally KV compression is becoming a core scaling primitive — not just for LLMs, but for video generation too. Paper: Code: (1/5)

🎥 Video generation is hitting the memory wall. As videos get longer, the KV cache quietly explodes — and long-horizon consistency starts to break. We built Quant VideoGen: a training-free KV cache compression method for auto-regressive video diffusion. Instead of storing every KV in high precision, QVG exploits video’s spatiotemporal redundancy with semantic-aware smoothing + progressive residual quantization. 🚀 Up to 7× KV memory reduction ⚡ <4% overhead ✅ Strong long-video quality 🕹️ Deploy HYWorldPlay on your own RTX 5090 locally KV compression is becoming a core scaling primitive — not just for LLMs, but for video generation too. Paper: Code: (1/5)

Haocheng Xi

65,694 просмотров • 4 месяцев назад

🚀New paper out - We present Video-MSG (Multimodal Sketch Guidance), a novel planning-based training-free guidance method for T2V models, improving control of spatial layout and object trajectories. 🔧 Key idea: • Generate a Video Sketch — a spatio-temporal plan with background, foreground, and motion in the pixel space. • Encode this structure directly into the latent space of the diffusion model during generation, which does not require fine-tuning or additional memory during inference. 🧵

🚀New paper out - We present Video-MSG (Multimodal Sketch Guidance), a novel planning-based training-free guidance method for T2V models, improving control of spatial layout and object trajectories. 🔧 Key idea: • Generate a Video Sketch — a spatio-temporal plan with background, foreground, and motion in the pixel space. • Encode this structure directly into the latent space of the diffusion model during generation, which does not require fine-tuning or additional memory during inference. 🧵

Jialu Li

35,060 просмотров • 1 год назад

Mesmerizing 45-degree infinity module of the GA3D project in action. The GA3D Project is a “side-project” driven by a passion for the RepRap 3D printer theme, aiming to extend the RepRap/DIY (Do-It-Yourself) spirit to other processes and fields. The basic machine consists of a frame with a fixed table, and a 3-axis Cartesian robotic arm with CoreXY kinematics crossed in the XY plane. A very simple tool changer is located at both ends of the robotic arm. This makes it a modular machine that can be easily converted into any machine. Video Credit: Alain D. Geiser #3dprinting #3dprinter #additivetechnology #additivemanufacturing #engineering #technology

Mesmerizing 45-degree infinity module of the GA3D project in action. The GA3D Project is a “side-project” driven by a passion for the RepRap 3D printer theme, aiming to extend the RepRap/DIY (Do-It-Yourself) spirit to other processes and fields. The basic machine consists of a frame with a fixed table, and a 3-axis Cartesian robotic arm with CoreXY kinematics crossed in the XY plane. A very simple tool changer is located at both ends of the robotic arm. This makes it a modular machine that can be easily converted into any machine. Video Credit: Alain D. Geiser #3dprinting #3dprinter #additivetechnology #additivemanufacturing #engineering #technology

Wevolver

69,687 просмотров • 2 месяцев назад

Introduce EAGLE, a new method for fast LLM decoding based on compression: - 3x🚀than vanilla - 2x🚀 than Lookahead (on its benchmark) - 1.6x🚀 than Medusa (on its benchmark) - provably maintains text distribution - trainable (in 1~2 days) and testable on RTX 3090s Playground: Blog: Code: ⚒️First Principle: Compression! Yi Ma We find that the sequence of second-top-layer features is compressible, making the prediction of subsequent feature vectors from previous ones easy by a small model. 🙏Acknowledge: This project is greatly inspired by the Medusa team (Tianle Cai @yli3521 Zhengyang Geng Hongwu Peng Tri Dao), the Lookahead team (Hao Zhang LMSYS Org), and others. Joint work with Yuhui Li and Chao Zhang

Introduce EAGLE, a new method for fast LLM decoding based on compression: - 3x🚀than vanilla - 2x🚀 than Lookahead (on its benchmark) - 1.6x🚀 than Medusa (on its benchmark) - provably maintains text distribution - trainable (in 1~2 days) and testable on RTX 3090s Playground: Blog: Code: ⚒️First Principle: Compression! Yi Ma We find that the sequence of second-top-layer features is compressible, making the prediction of subsequent feature vectors from previous ones easy by a small model. 🙏Acknowledge: This project is greatly inspired by the Medusa team (Tianle Cai @yli3521 Zhengyang Geng Hongwu Peng Tri Dao), the Lookahead team (Hao Zhang LMSYS Org), and others. Joint work with Yuhui Li and Chao Zhang

Hongyang Zhang

118,857 просмотров • 2 лет назад

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

MrNeRF

17,062 просмотров • 1 год назад

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

Sudo su

37,623 просмотров • 6 месяцев назад

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

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

Jonathan Stephens

17,712 просмотров • 8 месяцев назад

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)

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)

Chenfeng_X

60,103 просмотров • 4 месяцев назад

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 -

Abhishek Gupta

11,430 просмотров • 1 год назад

🚀 Welcome to Luxxcoin - Where Meme Culture Becomes Competition Luxxcoin ($LUX) is the first MemeFi project transforming memes into a real, rules-based on-chain competition. Every 60 days, the Hero Rewards engine ranks wallets through a transparent scoring model and automatically distributes $LUX to the top performers - all visible on a live leaderboard. Built on Solana. Audited. Multi-sig secured. Designed for long-term gameplay, not hype cycles. With a 7.5-year competitive structure, 45 reward rounds, and deterministic scoring, Luxxcoin introduces a new standard in meme culture: 🔸 Hold with strategy 🔸 Climb the leaderboard 🔸 Earn like a hero If you're here for the future of memes - competitive, transparent, and on-chain - welcome to the arena. 👑 The Meme Competition Has Begun. Join Luxx. Website: Community chat: #LuxxUp #MemeFi $LUX

🚀 Welcome to Luxxcoin - Where Meme Culture Becomes Competition Luxxcoin ($LUX) is the first MemeFi project transforming memes into a real, rules-based on-chain competition. Every 60 days, the Hero Rewards engine ranks wallets through a transparent scoring model and automatically distributes $LUX to the top performers - all visible on a live leaderboard. Built on Solana. Audited. Multi-sig secured. Designed for long-term gameplay, not hype cycles. With a 7.5-year competitive structure, 45 reward rounds, and deterministic scoring, Luxxcoin introduces a new standard in meme culture: 🔸 Hold with strategy 🔸 Climb the leaderboard 🔸 Earn like a hero If you're here for the future of memes - competitive, transparent, and on-chain - welcome to the arena. 👑 The Meme Competition Has Begun. Join Luxx. Website: Community chat: #LuxxUp #MemeFi $LUX

Luxxcoin

39,094 просмотров • 9 месяцев назад

𝐀𝐍𝐎𝐍𝐆 𝐏𝐑𝐎𝐁𝐋𝐄𝐌𝐀 𝐌𝐎 𝐄𝐇 𝐌𝐀𝐇𝐀𝐋 𝐃𝐈𝐍 𝐊𝐈𝐓𝐀 As we enter a 𝗻𝗲𝘄 𝗲𝗿𝗮 of 𝙔𝙤𝙪’𝙧𝙚 𝙈𝙮 𝙁𝙖𝙫𝙤𝙧𝙞𝙩𝙚 𝙎𝙤𝙣𝙜, we just want to sincerely thank everyone for the love and support you’ve shown. Thank you for streaming the 𝙏𝙚𝙠𝙖 𝙇𝙖𝙣𝙜 music video and helping it reach 𝟭.𝟳𝗠 𝘃𝗶𝗲𝘄𝘀 𝗮𝗻𝗱 𝗰𝗼𝘂𝗻𝘁𝗶𝗻𝗴. We truly appreciate every single one of you who took the time to watch, stream, and support the project. As a small way of giving back, here’s our little 𝙖𝙮𝙪𝙙𝙖 for all of you, a 𝗧𝗶𝗸𝗧𝗼𝗸 𝘃𝗶𝗱𝗲𝗼 𝗳𝗲𝗮𝘁𝘂𝗿𝗶𝗻𝗴 𝗔𝗻𝗴𝗲𝗹. We hope you enjoy it! 🥂 The question is, 𝙗𝙖𝙠𝙞𝙩 𝙠𝙖 𝙣𝙖𝙜𝙠𝙖𝙠𝙖𝙜𝙖𝙣𝙞𝙮𝙖𝙣? 𝙔𝙤𝙪’𝙧𝙚 𝙈𝙮 𝙁𝙖𝙫𝙤𝙧𝙞𝙩𝙚 𝙎𝙤𝙣𝙜, this 𝐉𝐮𝐧𝐞 on 𝐆𝐌𝐀.

𝐀𝐍𝐎𝐍𝐆 𝐏𝐑𝐎𝐁𝐋𝐄𝐌𝐀 𝐌𝐎 𝐄𝐇 𝐌𝐀𝐇𝐀𝐋 𝐃𝐈𝐍 𝐊𝐈𝐓𝐀 As we enter a 𝗻𝗲𝘄 𝗲𝗿𝗮 of 𝙔𝙤𝙪’𝙧𝙚 𝙈𝙮 𝙁𝙖𝙫𝙤𝙧𝙞𝙩𝙚 𝙎𝙤𝙣𝙜, we just want to sincerely thank everyone for the love and support you’ve shown. Thank you for streaming the 𝙏𝙚𝙠𝙖 𝙇𝙖𝙣𝙜 music video and helping it reach 𝟭.𝟳𝗠 𝘃𝗶𝗲𝘄𝘀 𝗮𝗻𝗱 𝗰𝗼𝘂𝗻𝘁𝗶𝗻𝗴. We truly appreciate every single one of you who took the time to watch, stream, and support the project. As a small way of giving back, here’s our little 𝙖𝙮𝙪𝙙𝙖 for all of you, a 𝗧𝗶𝗸𝗧𝗼𝗸 𝘃𝗶𝗱𝗲𝗼 𝗳𝗲𝗮𝘁𝘂𝗿𝗶𝗻𝗴 𝗔𝗻𝗴𝗲𝗹. We hope you enjoy it! 🥂 The question is, 𝙗𝙖𝙠𝙞𝙩 𝙠𝙖 𝙣𝙖𝙜𝙠𝙖𝙠𝙖𝙜𝙖𝙣𝙞𝙮𝙖𝙣? 𝙔𝙤𝙪’𝙧𝙚 𝙈𝙮 𝙁𝙖𝙫𝙤𝙧𝙞𝙩𝙚 𝙎𝙤𝙣𝙜, this 𝐉𝐮𝐧𝐞 on 𝐆𝐌𝐀.

TEAM CAPRICE OFFICIAL

64,180 просмотров • 3 месяцев назад

🚀 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

10,605 просмотров • 1 год назад

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 просмотров • 1 год назад