🦎 Did you know axolotls can regenerate complex body parts, including their brain? 🧠 Our new #AI model 𝗔𝘅𝗼𝗹𝗼𝘁𝗹𝟯𝗗 (#ECCV26) faithfully completes #3D objects based on partial views and incomplete point clouds: research.nvidia.com/labs/sil/pro... Kudos Anita Hu! #MachineLearning #NVIDIA

From Tasks to Topology: Dorsal and Ventral Streams Emerge in Optimized Neural Networks https://www.biorxiv.org/content/10.1101/2025.11.16.688720v1

🔍 New NVIDIA Spatial Intelligence Lab internship postings for 2026. Come work with us to advance foundational technologies that enable AI systems to model and interact meaningfully with the world! Topics on our homepage: research.nvidia.com/labs/sil/ Application link below

NVIDIA Spatial Intelligence Lab (SIL)

Advancing foundational technologies enabling AI systems to perceive, model, and interact with the world.

research.nvidia.com

Join us at #CVPR2025 for a preview of this #NVIDIA tech during a live-coding session. A #GPU back end will be reserved for all attending – just don’t forget to bring your laptop for some hands-on fun! Wed, Jun 11, 8am-noon, or join in at 10:20 after the break. tinyurl.com/nv-kaolin-cv...

🔊 New NVIDIA paper: Audio-SDS 🔊 We repurpose Score Distillation Sampling (SDS) for audio, turning any pretrained audio diffusion model into a tool for diverse tasks, including source separation, impact synthesis & more. 🎧 Demos, audio examples, paper: research.nvidia.com/labs/toronto... 🧵below

What if you could control the weather in any video — just like applying a filter? Meet WeatherWeaver, a video model for controllable synthesis and removal of diverse weather effects — such as 🌧️ rain, ☃️ snow, 🌁 fog, and ☁️ clouds — for any input video.

🦙New #NVIDIA paper: LLaMA-Mesh 🦙 We enable LLMs to generate 3D meshes by representing them as plain text and fine-tuning, unifying 3D and text modalities in a single model. 🔎 Webpage research.nvidia.com/labs/toronto... 🕹️ Interactive Demo huggingface.co/spaces/Zheng... 💾 Model checkpoint available

Check out our new #NVIDIA paper: ⚡️Multi-student Diffusion Distillation ⚡️ We make single-step distilled generators better and faster using our new method, multi-student distillation (MSD)! Explore the project page to learn more: research.nvidia.com/labs/toronto...

@yannnke.bsky.social · 2y ago

New #NVIDIA paper to make diffusion models better and faster 🚀 Multi-Student Distillation! We distill diffusion models into multiple 1-step students, allowing (a) improved quality by specializing in subsets and (b) improved latency by distilling into smaller architectures. 1/n

🚨 New #NeurIPS2025 paper “Training Data Attribution via Approximate Unrolling” 🚨 Introducing SOURCE: A method to understand how individual training examples influence neural net behavior, allowing us to make AI models more transparent and trustworthy! 📄 Full paper: openreview.net/pdf?id=3NaqG...