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Check out our 2025 highlights in computer vision! 🚀Five new *St3R models (MASt3R-SfM, MUSt3R, PanSt3R, HAMSt3R, HOSt3R ) 🤩Anny parametric 3D human model (Apache 2.0) 🤟Universal encoder for all-in-one vision FM More info ▶️

NAVER LABS Europe

5,268 subscribers

12,587 views • 10 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

2 Comments

Himanshu Kumar10 months ago

NAVER's computer vision advancements include multiple new *St3R models and a 3D human model.

Shi Boao10 months ago

great work! Will these project be open-sourced?

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454,110 views • 3 years ago

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AI at Meta

163,602 views • 1 year ago

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QVAC

15,330,405 views • 2 months ago

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123,641 views • 1 year ago

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a16z

40,937 views • 1 month ago

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12,613 views • 2 months ago

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43,850 views • 1 year ago

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16,363 views • 1 year ago

🚀 New update for the Grafilab AI ecosystem! We’ve just updated new frontier models in our Playground and Unified API, including the highly anticipated models by DeepSeek and Qwen families! 🧠⚡ But that’s not all… you can now run Side-by-Side Model Comparisons directly in the Grafilab Playground! 🥊 Compare outputs from Gemini, DeepSeek, Qwen, and more in real-time, on the exact same prompt, with zero code. Find the perfect model for your workload instantly. Try it out now:

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805,542 views • 3 months ago

We’re excited to share our groundbreaking new audio-driven performance model, featuring hyper-real expressions in near real-time. Any length video, in any style, is ready in 6 seconds or less—in HD. And we’ve managed to make it 20x faster and cheaper 💅 It’s all part of our vision to make AI video accessible and fun for more people—a way for them to connect, not just create. Rolling out soon in the Pika social app.

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1,243,932 views • 1 year ago

Got five papers accepted by #ECCV2024 European Conference on Computer Vision #ECCV2026 ! Huge thanks to all my collaborators! 😃 See you in Milan 🇮🇹 Summary of Selected Works (I made a fast-forward for them 😄) - [Shape Generation] Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models, ECCV 2024. - [Efficient Motion Generation] EMDM: Efficient Motion Diffusion Model for Fast, High-Quality Human Motion Generation, ECCV 2024. - [Controllable Motion Generation] TLControl: Trajectory and Language Control for Human Motion Synthesis, ECCV 2024. - [Avatar Generation] Disentangled Clothed Avatar Generation from Text Descriptions, ECCV 2024. Project Page: Surf-D: EMDM: TLControl: SOSMPL:

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18,192 views • 2 years ago

🤗 MOSS-VL-Realtime is now open source on Hugging Face . Built for real-time visual understanding over continuous video streams: 🧠 11B vision-language model 📜 Apache-2.0 license 💬 Ask questions at any point in a video stream 👀 Keeps watching while generating a response 🔄 Revises or interrupts its response as the scene changes 🤫 Can remain silent when more evidence is needed 🧩 256K-token context window 🌐 Chinese and English multimodal understanding 📦 Base, Instruct, and Realtime models are all open source From “watch first, answer later” to “keep watching while answering.” 👀 OpenMOSS Thank you SGLang LMSYS Org for day-0 support! 🚀

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MOSI

53,697 views • 3 months ago

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

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249,798 views • 3 years ago

🚨 BREAKING: Big news in the computer vision world! 🎥 Luxonis | Robotic Vision just dropped its new OAK 4 line, and it’s a big upgrade for edge computer vision. Instead of being “just a stereo camera,” OAK 4 is a fully standalone vision computer with 52 TOPS of on-device AI. Models run locally, depth is computed locally, and no external PC or cloud pipeline is required. This is why robotics teams love it: lower latency, lower cost, fewer failure points in the field. The hardware is built for the real-world. IP67, shock-resistant, wide-FOV RGB + stereo pair, IR projection, IMU, audio, and a patent-pending calibration system that keeps depth accurate even when conditions change. But the real move is the platform. With Luxonis Hub, you can deploy models, grab telemetry, push OTA updates, or collect data when performance drifts, all from a unified interface. It turns a single device into an end-to-end edge CV system. Most customers today in robotics are groups who just want something that works: AMRs, bin-picking systems, trailer-loading robots, and ag-tech. 🤖 And they all say the same thing, the appeal isn’t raw TOPS, it’s the all-in-one simplicity that lets them scale without building custom infrastructure. Feels like the direction edge vision has been waiting for: rugged hardware + high-throughput on-device compute + a real management layer. A next step toward “plug-and-deploy” perception for robots. 🔗 Find out more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

🚨 BREAKING: Big news in the computer vision world! 🎥 Luxonis | Robotic Vision just dropped its new OAK 4 line, and it’s a big upgrade for edge computer vision. Instead of being “just a stereo camera,” OAK 4 is a fully standalone vision computer with 52 TOPS of on-device AI. Models run locally, depth is computed locally, and no external PC or cloud pipeline is required. This is why robotics teams love it: lower latency, lower cost, fewer failure points in the field. The hardware is built for the real-world. IP67, shock-resistant, wide-FOV RGB + stereo pair, IR projection, IMU, audio, and a patent-pending calibration system that keeps depth accurate even when conditions change. But the real move is the platform. With Luxonis Hub, you can deploy models, grab telemetry, push OTA updates, or collect data when performance drifts, all from a unified interface. It turns a single device into an end-to-end edge CV system. Most customers today in robotics are groups who just want something that works: AMRs, bin-picking systems, trailer-loading robots, and ag-tech. 🤖 And they all say the same thing, the appeal isn’t raw TOPS, it’s the all-in-one simplicity that lets them scale without building custom infrastructure. Feels like the direction edge vision has been waiting for: rugged hardware + high-throughput on-device compute + a real management layer. A next step toward “plug-and-deploy” perception for robots. 🔗 Find out more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

41,955 views • 10 months ago

We built the fastest PDF -> markdown parser in the world 🚀⚡️ AND it’s more accurate than any other open-source, model-free parser (pymupdf4llm, opendataloader, pdf-inspector, markitdown) on 3 standardized benchmarks: olmOCR0-bench, opendataloader-bench, ParseBench Introducing LiteParse v2.1. The v2 base version was already the fastest document->text parser on the planet, and with this new release we’ve introduced markdown. It is fully open-source (Apache 2.0) and free, is usable from CLI/Rust/Node/Python/WASM, and is also installable as a one-click agent skill. Check it out: Come check out LiteParse:

We built the fastest PDF -> markdown parser in the world 🚀⚡️ AND it’s more accurate than any other open-source, model-free parser (pymupdf4llm, opendataloader, pdf-inspector, markitdown) on 3 standardized benchmarks: olmOCR0-bench, opendataloader-bench, ParseBench Introducing LiteParse v2.1. The v2 base version was already the fastest document->text parser on the planet, and with this new release we’ve introduced markdown. It is fully open-source (Apache 2.0) and free, is usable from CLI/Rust/Node/Python/WASM, and is also installable as a one-click agent skill. Check it out: Come check out LiteParse:

Jerry Liu

364,182 views • 3 months ago

Introducing Prompt-to-leaderboard (P2L): a real-time LLM leaderboard tailored exactly to your use case! P2L trains an LLM to generate "prompt-specific" leaderboards, so you can input a prompt and get a leaderboard specifically for that prompt. The model is trained on the 2M human preference votes from Chatbot Arena. P2L Highlights: 🔹Instant leaderboard for any prompt 🗿 🔹Optimal model routing (hit #1 on Chatbot Arena in Jan 2025 with 1395 score 🧏) 🔹Fine-grained model strength & weakness analysis 🤓 Check out our demo and thread below for more details!

Introducing Prompt-to-leaderboard (P2L): a real-time LLM leaderboard tailored exactly to your use case! P2L trains an LLM to generate "prompt-specific" leaderboards, so you can input a prompt and get a leaderboard specifically for that prompt. The model is trained on the 2M human preference votes from Chatbot Arena. P2L Highlights: 🔹Instant leaderboard for any prompt 🗿 🔹Optimal model routing (hit #1 on Chatbot Arena in Jan 2025 with 1395 score 🧏) 🔹Fine-grained model strength & weakness analysis 🤓 Check out our demo and thread below for more details!

Arena.ai

122,599 views • 1 year ago

We achieved a breakthrough at BTL. AI models are very linear. They think in one direction at a time, and when they're wrong they scramble and start again. For something meant to replace humans, that's way too human. We changed that. We made LLM reasoning and execution work more like a quantum computer: many paths at once, the ones that meet merge into one, the dead ends cancel out, and everything left moves forward together. On the same hard problems, a 1.7B model thinking the normal way solved 3 out of 30. Interference Search(our new architecture)solved 23. We watched the normal model find the right answer at token 1,313, check it 9 more times, wander off and run out of budget without ever answering. Ours got there in 3 steps. Parallel thinking and execution. Subagents were a terrible way to tackle this. More technical details soon.

We achieved a breakthrough at BTL. AI models are very linear. They think in one direction at a time, and when they're wrong they scramble and start again. For something meant to replace humans, that's way too human. We changed that. We made LLM reasoning and execution work more like a quantum computer: many paths at once, the ones that meet merge into one, the dead ends cancel out, and everything left moves forward together. On the same hard problems, a 1.7B model thinking the normal way solved 3 out of 30. Interference Search(our new architecture)solved 23. We watched the normal model find the right answer at token 1,313, check it 9 more times, wander off and run out of budget without ever answering. Ours got there in 3 steps. Parallel thinking and execution. Subagents were a terrible way to tackle this. More technical details soon.

Bad theory labs

173,138 views • 19 days ago

Introducing the future of Prism and GameGPT. For the next 3 days, we’ll share an updated view on our product vision for 2024. With so much insider info coming, you won’t want to miss it! Let’s get started: To start, as you can see in the video, we’ve been hard at work quietly building the Prism Flywheel. The drops won’t stop this year with so much in progress: - New site (check out at for new links) - New Challenge Integrations (Valorant / CS2 are next) - New weekly drops in the Arcade - First co-developed partnership game launching VERY soon - Web3 game studios integrating their games into our Arcade - Genesis NFT Mint - Genesis NFT Cinematic - The Much Awaited GameGPT Product Demo - GameGPT Closed Alpha - GameGPT Creator’s Program season 0 - Prism Mobile App - New Blockchain integrations - And MUCH MORE We hope you’re ready for a jam-packed 2024. Look for our post tomorrow as we drop more info including an updated roadmap (with dates) for all of the events listed above.

Introducing the future of Prism and GameGPT. For the next 3 days, we’ll share an updated view on our product vision for 2024. With so much insider info coming, you won’t want to miss it! Let’s get started: To start, as you can see in the video, we’ve been hard at work quietly building the Prism Flywheel. The drops won’t stop this year with so much in progress: - New site (check out at for new links) - New Challenge Integrations (Valorant / CS2 are next) - New weekly drops in the Arcade - First co-developed partnership game launching VERY soon - Web3 game studios integrating their games into our Arcade - Genesis NFT Mint - Genesis NFT Cinematic - The Much Awaited GameGPT Product Demo - GameGPT Closed Alpha - GameGPT Creator’s Program season 0 - Prism Mobile App - New Blockchain integrations - And MUCH MORE We hope you’re ready for a jam-packed 2024. Look for our post tomorrow as we drop more info including an updated roadmap (with dates) for all of the events listed above.

GameGPT

436,878 views • 2 years ago

Today we're announcing microSLAM, a monocular SLAM system built by our computer vision team in Zürich with ETH Zurich's Computer Vision and Geometry Lab. It ranks first on the LaMaria benchmark for monocular SLAM, and holds up against systems that carry many more cameras and IMUs. Ours runs on a single RGB stream. Robot learning is bottlenecked on data, and the largest untapped source is people going about their work. A camera on someone's head for an afternoon in a plant is a record of how that plant actually operates, including all the parts nobody writes down. That data is only useful if you can recover the geometry, and geometry from one moving camera in a world that will not sit still is the hard version of the problem. Solving it monocular is what makes the data cheap. Rigs do not scale to every worker. Glasses do. microSLAM turns that footage into interactive 3D environments where behavioral foundation models can be trained. One person walks a factory floor. A robot learns to navigate it.

Today we're announcing microSLAM, a monocular SLAM system built by our computer vision team in Zürich with ETH Zurich's Computer Vision and Geometry Lab. It ranks first on the LaMaria benchmark for monocular SLAM, and holds up against systems that carry many more cameras and IMUs. Ours runs on a single RGB stream. Robot learning is bottlenecked on data, and the largest untapped source is people going about their work. A camera on someone's head for an afternoon in a plant is a record of how that plant actually operates, including all the parts nobody writes down. That data is only useful if you can recover the geometry, and geometry from one moving camera in a world that will not sit still is the hard version of the problem. Solving it monocular is what makes the data cheap. Rigs do not scale to every worker. Glasses do. microSLAM turns that footage into interactive 3D environments where behavioral foundation models can be trained. One person walks a factory floor. A robot learns to navigate it.

Gianni Hodel

71,409 views • 1 month ago