Selena Ling 凌子涵

Our #Siggraph25 work found a simple, nearly one-line change that greatly eases neural field optimization for a wide variety of existing representations. “Stochastic Preconditioning for Neural Field Optimization” w/ @merlin_ND @_AlecJacobson @nmwsharp

Our short film Dear Upstairs Neighbors is previewing at @sundancefest. 🎬 It’s a story about noisy neighbors, but behind the scenes, it’s about solving a huge challenge in generative AI: control. Developed by Pixar alumni, an Academy Award winner, researchers, and engineers, here’s how it came together. 🎨

This is absolutely shameful. Agents of a federal agency unnecessarily escalating, and then executing a defenseless citizen whose offense appears to be using his cell phone camera. Every person regardless of political affiliation should be denouncing this.

Did Blender help you this year? Help back! If every active user contributed $5 this month, Blender would be funded for the entire year 2026. Professional 3D software. No subscriptions. No limits. Just your support. Do your part. Donate today. blender.org/news/give-back… #b3d

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: research.nvidia.com/labs/toronto-a… Code and Models: github.com/nv-tlabs/lyra Paper: arxiv.org/abs/2509.19296

📢📢📢 3D Gaussian Flats @ #NeurIPS2025 A hybrid 2D/3D representation that reconstructs photorealistic scenes. 🔗 Project: theialab.github.io/3dgs-flats 📄 ArXiv: arxiv.org/abs/2509.16423 💻 Code: github.com/theialab/3dgs-…

Season 1 of Toronto School of Foundation Modelling kicks off this Thursday at New Stadium!!! 60 people will be attending weekly sessions for 3 months, learning to build Foundation Models from scratch. Around 10 guest speakers (more to come) will be flying to Toronto to talk about what they do best. I'm grateful for the support of Cohere, New, Vengeance and all the donors. I will make this a series worth your time. And apologies to the people I didn't get back to - spots are all taken but will reach out if that changes.

I’m excited to announce that our paper, “Learning Riemannian Metrics for Interpolating Animations,” has been accepted to GSI 2025! 🧵 co-authored with @vm2358 at @UofT and @ninamiolane at the @UCSB @geometric_intel lab! gi.ece.ucsb.edu/node/217

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“Everyone knows” what an autoencoder is… but there's an important complementary picture missing from most introductory material. In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵

Check out our new paper on robust motion segmentation! Wanna run your SfM pipeline on dynamic scenes? Consider using our RoMo masks to get improvements!! 🚀

For folks in the @siggraph community: You may or may not be aware of the controversy around the next #SIGGRAPHAsia location, summarized here: cs.toronto.edu/~jacobson/webl… If you're concerned, consider signing this letter: docs.google.com/document/d/1ZS… via this form docs.google.com/forms/d/e/1FAI…

We show many more experiments across different implicit surface representations in our paper. Please check out our #SGP25 paper here arxiv.org/pdf/2506.05268 and reach out if you have any questions! Code coming soon! (9/9)

With uniformly sampled points, one can also easily perform importance sampling using curvature or other quantities like losses, and construct geometry-aware regularization terms to improve neural implicit optimization. (8/9)

Our #SGP25 work studies a simple and effective way to uniformly sample implicit surfaces by casting rays. (1/9) “Uniform Sampling of Surfaces by Casting Rays” w/ @_abhishekmadan @nmwsharp @_AlecJacobson