Nick Sharp

Excited to share FreeForm☁️: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes at #CVPR2026 FreeForm enables fast elastodynamic simulation for robotics and beyond, directly on messy data (no mesh needed)!

📣 Participant Applications Open: LOGML’26, Imperial College London (13–17 July) A week on geometry/topology + ML talks, tutorials, projects & networking. 👩‍🎓 Students/PhD/Postdocs 💰 Limited fee waivers & travel support 🗓 Deadline: 1 May 2026 🔗 Apply: forms.gle/RjxJHCdKVJF41V…

We scaled up Lyra to generate explorable 3D worlds! 🚀 Introducing Lyra 2.0 — turning a single image into a 3D world you can walk through, look back, and even drop a robot into 🤖 Code and Model available today! 🌐 Website: research.nvidia.com/labs/sil/proje… (1/N)

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: research.nvidia.com/labs/sil/proje… 📄 Read the paper: arxiv.org/abs/2604.13036

Excited to share our new work at CVPR 2026: Learning Convex Decomposition via Feature Fields. We introduce the first feedforward openworld model that generates high-quality convex decomposition for any 3D shapes in seconds, enabling faster simulation. 🔗research.nvidia.com/labs/sil/proje…

HiT: Hierarchical Transformers for Unsupervised 3D Shape Abstraction - Project: aditya-vora.github.io/HiT/ - Paper: arxiv.org/abs/2510.27088 - Code: github.com/aditya-vora/HiT We will present HiT at @3DVconf Poster 5-27. Join us if you are around!

HiT: Hierarchical Transformers for Unsupervised 3D Shape Abstraction - Project: aditya-vora.github.io/HiT/ - Paper: arxiv.org/abs/2510.27088 - Code: github.com/aditya-vora/HiT We will present HiT at @3DVconf Poster 5-27. Join us if you are around!

Excited to share our new work at CVPR 2026: Learning Convex Decomposition via Feature Fields. We introduce the first feedforward openworld model that generates high-quality convex decomposition for any 3D shapes in seconds, enabling faster simulation. 🔗research.nvidia.com/labs/sil/proje…

We don't expect LLMs to multiply numbers or sort lists directly within their output token stream. Instead, we ask them emit code and execute it in a separate runtime. Why predict the opposite outcome for simulating interactive worlds? worldlabs.ai/blog/3d-as-code

In my recent blog post, I argue that "vision" is only well-defined as part of perception-action loops, and that the conventional view of computer vision - mapping imagery to intermediate representations (3D, flow, segmentation...) is about to go away. vincentsitzmann.com/blog/bitter_le…

Can we apply gradient descent to discrete changes? In our new #SIGGRAPHAsia paper, we show that gradient descent can work on shape grammars, as in CAD and procedural modeling, but only if the grammars are designed correctly!

Can we apply gradient descent to discrete changes? In our new #SIGGRAPHAsia paper, we show that gradient descent can work on shape grammars, as in CAD and procedural modeling, but only if the grammars are designed correctly!