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.

🚀 Announcing meshgen (AI in Blender) Update 0.6 🤗 Now supports remote backend via litellm, e.g. Hugging Face, ollama 🎨 UI/UX overhaul This lays the foundation for Blender agents and more advanced 3D AI models (coming this year)

🧊 New dataset of 50k+ low poly obj meshes 🎢 objaverse subsampled to <500 poly models, converted to untextured objs 🔧 suitable for training autoregressive transformer-based 3D models, which have limited context length, such as LLaMA-Mesh

Here's a recap of what happened in AI 3D in 2024

🦙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...

🚨 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...