Gen-3 Alpha exhibits several simulation capabilities, including the ability... to generate dynamic camera motions, complex fluid motion, and interactions between objects. We expect further simulation capabilities to emerge as we continue to scale our models. To learn more about our long-term research efforts to build General World Models, visit: Prompt: A cinematic top down shot of cold water being poured into a hot frying pan. (1/5)show more

New Generation Model! 🚨 We're introducing the o3m model... to our expanding lineup of generation models. While still in its beta phase, we're actively refining its output to push its capabilities even further. So far, we've observed a significant leap in physics calculations, AI-driven mechanics, and the overall fluidity of app and game interactions. As we continue to tweak and optimize, expect even greater advancements in precision, automation, and creative execution.show more

Today we're excited to share updates to Gen-3 Alpha... Turbo that bring more control and the ability to generate vertical videos. Learn more below. (1/4)show more

Having seen a few comments, I’d like to clarify... something. We haven’t used generative AI in the design team. We have the human capabilities to create what we want, and when our capabilities are limited, we learn and strive to achieve our goalsshow more

We are excited to launch our two models Pharia-1-LLM-7B-control... and Pharia-1-LLM-7B-control-aligned. Both models and the code used to train them are now publicly available and open-sourced for non-commercial research and educational use. Read our model blog post here: Learn more about our open-source codebase Scaling: #writtenbyalephalphashow more

Sorry but this isn't even close to a realistic... simulation. This Three.js water simulation took weeks of effort, hundreds of prompts, and I needed to pull on my CS and physics background to understand where the AI was failing (which happens often). Maybe some day agents will be able to generate complex simulations in a single prompt but for now they can only generate toy prototypes.show more

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical... Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.show more

Can we use motion to prompt diffusion models? Our... #ICLR2024 paper does just that. We propose Motion Guidance, a technique that allows users to edit an image by specifying “where things should move.”show more

We are entering an extremely exciting era for open-weight... models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.show more

World, meet our new brand. We’ve strengthened our consulting... capabilities, dramatically expanded our design offer, and broadened our experience practice significantly to include retail, culture and workspace. We build brands designed to transform.show more

Introducing ✨ Aya Vision ✨ - an open-weights model... to connect our world through language and vision Aya Vision adds breakthrough multimodal capabilities to our state-of-the-art multilingual 8B and 32B models. 🌿show more

We have 6 different models of CNC machines already... designed and validated for production but we would need crores to scale up production and offer it to other manufacturers. Hence, we only build machines for our own production facility. Capital is a bottleneck for us. Currently we just have a tiny space within our own factory which is our R&D lab. We are a team of mere three people. Scaling hardware isn’t easy but it’s something that must be done for us to truly be self-reliant. Really appreciate your efforts Sridhar Vembu to build these hardware tools here. We must build more factories and hardware products in the country. Long way to go before I sleep.show more

Landed in Canberra to meet with Prime Minister Anthony... Albanese. This is the first bilateral visit by a Canadian PM to Australia in two decades. In a more uncertain world, we turn to our long-standing partners who share our values and our ambition to build.show more

Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering discuss: The... correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong generative and inpainting capabilities, we find that current models do not sufficiently "understand" the scene shown in a single picture to generate consistent lighting effects (shadows, bright reflections, etc.) while preserving the identity and details of the composited object. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes. Our physically based pipeline further enables automatic materials and tone-mapping refinement.show more

(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 licensesshow more

We continue to learn, to grow, to expand our... territory, sphere of influence and build bridges across generations, class, tribe and ideology. It has been a long journey, it has been an amazing journey. Together we can. #ourlagosshow more

Today, we released Lyra 2.0, a framework for generating... persistent, explorable 3D worlds at scale, from NVIDIA Research. Generating large-scale, complex environments is difficult for AI models. Current models often “forget” what spaces look like and lose track of movement over time, causing objects to shift, blur, or appear inconsistent. This prevents them from creating the reliable 3D environments required for downstream simulations. Lyra 2.0 solves these issues by: ✅ Maintaining per-frame 3D geometry to retrieve past frames and establish spatial correspondences ✅ Using self-augmented training to correct its own temporal drifting. Lyra 2.0 turns an image into a 3D world you can walk through, look back, and drop a robot into for real-time rendering, simulation, and immersive applications. ➡️ Learn more: 📄 Read the paper:show more

1/ Happy to share VADER: Video Diffusion Alignment via... Reward Gradients. We adapt foundational video diffusion models using pre-trained reward models to generate high-quality, aligned videos for various end-applications. Below we generated a short movie using VADER 😀, we used ChatGPT to write a script and an off-the-shelf AI music generator to generate the sound. Our code & weights are open-sourced:show more

Seedream 4.0 supports deep intention understanding. More powerful inference... capabilities, enabling cross-time and space prediction and simulation to make the unseen visible. Learn More: #Seedream #AI #GenAI #BytePlus #ByteDanceshow more

Artist & animator yosu shows how to create a... zolly shot! Learn how to use keyframes to create cinematic camera motions with Clip Studio Paint!show more