
3D geometry researcher: graphics, vision, 3D ML, etc | Senior Research Scientist @NVIDIA | running, hockey, baking, & cheesy sci fi | opinions my own | he/him
Seattle, WA
Joined May 2018
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Nick Sharp retweeted
In this era of artificial intelligence, it is becoming urgent to distinguish human art from what machines produce. There is an ontological difference, even before an aesthetic one, between art and what a machine can generate through statistical calculation based on millions of images created by others. Algorithms lack the spark of humanity. For this reason, the Church wishes to renew an alliance with artists and cultural institutions to safeguard our humanity.
Nick Sharp retweeted
The era of Physical AI is coming, and I firmly believe that embodied intelligence will not emerge from imitation learning alone. Just as humans first learn by observing the world and then build deeper understanding through interaction, the breakthrough moment for Physical AI will come through interactive learning.
Real-world trial and error is too slow, expensive, and unsafe. To scale interactive learning, we need to recreate the richness of reality without being constrained by reality itself. We need simulated reality—a place where embodied intelligence can experience situations, make mistakes, learn from them, and try again millions of times before encountering them in the real world.
I am incredibly excited to build @VeedaAI alongside my co-founders, @FidlerSanja and @HuanLing6 , and an extraordinary group of researchers and engineers. Together, we are developing the next generation of interactive foundation world models for Physical AI.
There is no team I would rather take on this challenge alongside, and I couldn’t be more excited for what we’re going to build.
veeda.ai
I’m incredibly excited to introduce @VeedaAI , which I co-founded with my incredible longtime collaborators @ZGojcic and @HuanLing6 .
We started Veeda because we believe robotics will reshape the world—changing how we move people and goods, how we manufacture and build, and how we operate in the physical world.
We also firmly believe that the scaling moment for Physical AI will come from robots learning through interaction with the world.
Just like humans learn from interaction with their environments by trying, failing and trying again, until we succeed. And just like the capabilities of LLMs were truly unlocked when they started training in interactive environments and not only on vast amounts of internet text.
For Physical AI, the central challenge is making this kind of interactive learning possible at scale. The real world is simply not a practical training ground for robots to learn through trial and error. It is unsafe, expensive, and time-consuming. To scale interactive learning, robots will need to learn in simulated reality.
At Veeda, our sole mission is to build simulated reality for Physical AI. Our conviction is that this will become the critical infrastructure layer for all areas of robotics.
As envisioned in pop culture, this means building “the Matrix” for Physical AI, where, through a virtual embodiment, robot intelligence can interact with an entirely virtual world in a million possible ways, learning from its own mistakes.
We believe that World Models are the foundational technology that can make this possible. These generative models learn from massive amounts of sensor and physical-world data to reach the quality, diversity, and physical realism of the simulated reality that robots require.
I could not be more excited to take on this challenge and build this transformative technology alongside an extraordinary team of engineers and researchers who have spent years working on some of the hardest problems across simulation, generative AI, 3D, robotics, and embodied intelligence.
Together with our investors at @khoslaventures and @radicalvcfund , we intend to push the frontier of this technology and build the foundational infrastructure for interactive robotics learning and evaluation at scale.
Come build this future with us.
And to roboticists, help us understand what you need, we’re building this for you.
veeda.ai
🎉 Excited to introduce TRON, a relighting framework for 3D captures.
💡TRON pairs a neural renderer with 3D Gaussian reconstructions, achieving realistic quality, with 3D, material, & lighting control at interactive frame rates.
arxiv.org/abs/2606.11314
research.nvidia.com/labs/sil…
Nick Sharp retweeted
Releasing Walk on Spheres Extensions (WoSX): a GPU-accelerated C++/Python library for Monte Carlo physics simulation on complex geometry
Think path tracing but for physics beyond light transport: heat, electrostatics, potential flow, deformation & more!
github.com/nv-tlabs/wosx
Nick Sharp retweeted
Paper announcement📢 GimmBO: Interactive Generative Image Model Merging via Bayesian Optimization
Chenxi Liu, Selena Ling (@seleniumlzh), Alec Jacobson
🏆 SIGGRAPH 2026 Best Paper
Selena and I will be presenting in LA (July 19-23)!
🌐 Project: gimmbo-project.github.io/
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Nick Sharp retweeted
Code is out! Feel free to try it here: github.com/nv-tlabs/learning…
We’ll also be presenting our work as a CVPR oral at Bluebird Ballroom on Sunday, June 7, at 3:00 PM. Hope to see you there!"
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…
Nick Sharp retweeted
Seeing how realistic video generation has become, I always imagined a model that continuously constructs a 3D world as you move through previously unseen environments. Today, I’m excited that we’ve made it real!
Lyra 2 turns images into explorable 3D worlds—and the environment generated along your trajectory can be exported as 3D Gaussian Splatting and meshes, making it directly usable for real-time rendering and physics simulations.
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…
(1/N)
Nick Sharp retweeted
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…
(1/N)
Nick Sharp retweeted
📢want to produce realistic dynamic 3d worlds (with >100 splats)
my new NVIDIA internship project, VoMP, is the first feed forward approach to convert input surface geometry to volumetric sim-ready assets by assigning real world physics materials
🌐Project: research.nvidia.com/labs/sil…
📜Paper: arxiv.org/abs/2510.22975
Nick Sharp retweeted
See your Gaussian Splats deform and collide under gravity! #NVIDIA Kaolin Library just released v0.18.0. github.com/NVIDIAGameWorks/k…
Join us at #SIGGRAPH tomorrow Sunday, Aug 10, Room 121-122 for a hands-on lab showcasing this and an intro to NVIDIA Warp, used under the hood.
Nick Sharp retweeted
New at #SIGGRAPH2025:
Can we make Perlin Noise stretch along some underlying vector field? Well it turns out it's possible with two simple additions to the original method! No need for advection or convolutions.
Find the paper and implementations here:
github.com/jakericedesigns/S…
Nick Sharp retweeted
While making some figures for SGI* this year, I made some "behind the scenes" footage of how they get made: youtube.com/playlist?list=PL…
Basically a video extension of cs.cmu.edu/~kmcrane/faq.html ("figures?")
*SGI is a great program run by @JustinMSolomon & deserves more funding
Nick Sharp retweeted
Curious about 3D Gaussians, simulation, rendering and the latest from #NVIDIA? Come to the NVIDIA Kaolin Library live-coding session at #CVPR2025, powered by a cloud GPU reserved especially for you. Wed, Jun 11, 8-noon. Bring your laptop! tinyurl.com/nv-kaolin-cvpr25
Nick Sharp retweeted
FlexiCubes is now under Apache 2.0! 🎉
We've been excited to see FlexiCubes extracting high-quality meshes across the community in projects like TRELLIS and TripoSF --- now it's available with a more permissive license.
Let's keep building. 💙
👉 Flexicubes is in NVIDIA Kaolin: github.com/NVIDIAGameWorks/k…
👉 Or a standalone repo github.com/nv-tlabs/FlexiCub…
Nick Sharp retweeted
🚀Excited to release PartField—a feedforward model that learns part-based feature fields for 3D shapes!
It enables lightning-fast⚡️, robust, open-world hierarchical 3D part seg and unlocks cross-shape applications like co-seg and correspondence!
🔗shorturl.at/HnUmc 1/n
Nick Sharp retweeted
Announcing SGI 2025! Undergrads and MS students: Apply for 6 weeks of paid summer geometry processing research. No experience needed: 1 week tutorials + 5 weeks of projects. Mentors are top researchers in this emerging branch of graphics/computing/math. sgi.mit.edu
Nick Sharp retweeted
Interested in generating meshes using generative models? Our latest paper, SpaceMesh, tackles this challenge by focusing on directly creating manifold meshes. Super thrilled to be part of this amazing team! Check out the details below ⬇️
Generating nice meshes in AI pipelines is hard. Our #SIGGRAPHAsia2024 paper proposes a new representation which guarantees manifold connectivity, and even supports polygonal meshes -- a big step for downstream editing and simulation. (1/N)
SpaceMesh: research.nvidia.com/labs/tor…
Joint work with @mli0603 @MarcTLaw @matanatzmon @FidlerSanja @james_r_lucas @JunGao33210520 @nmwsharp (1/n)
Nick Sharp retweeted
Our CDTs (Centers for Doctoral Training) are great program-based opportunities for fully-funded PhD positions. Specifically, consider the Machine-Learning CDT:
mlsystems.uk/
If you are interested and specifically want to work with me on ML+geometry, contact me!
Nick Sharp retweeted
NVIDIA's Academic Grant Program now accepting its 2nd round of proposals in:
✅ Data Science
✅ Graphics & Vision
✅ Edge AI
#GenAI, #LLM, and Simulation & Modeling proposals are still welcome.
⌛ Apply now, deadline is Dec 31: nvda.ws/4dpBtgp #NVIDIAAcademicGrant


























