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How can we learn to generate 3D scenes directly with diffusion models if we only have images, no ground-truth 3d scenes? Ayush, Tianwei and George will tell you at our poster “diffusion with Forward Models”, #202!

Introducing pixelSplat: feed-forward Gaussian splats from image pairs! Led by David Charatan and Lester Li, collaborating with Andrea Tagliasacchi 🇨🇦! We propose a memory-efficient, fast and editable alternative to pixelNeRF based on 3D Gaussian Splatting! davidcharatan.com/pixelsplat/ 1/n

📢📢📢 introducing 𝐩𝐢𝐱𝐞𝐥𝐒𝐩𝐥𝐚𝐭: feed-forward Gaussian splats from image pairs! pixelNeRF + 3D Gaussian Splatting == davidcharatan.com/pixelsplat Led by David Charatan and S. Lester Li, collaboration with Vincent Sitzmann

.Tong’s ReparamModule is also how we made differentiable updates work with a learnable step size in our Dataset Distillation paper. It essentially allows you to take an existing pytorch module f_\theta(x) and reparameterize it as f(x, \theta). Very useful!

Introducing “FlowMap”, the first self-supervised, differentiable structure-from-motion method that is competitive with conventional SfM like Colmap! cameronosmith.github.io/flowmap/ IMO this solves a major missing piece for internet-scale training of 3D Deep Learning methods. 1/n

Let’s talk about the first lossless dataset this afternoon. Poster 109

I will be giving a talk at the Neural Rendering Intelligence workshop today at 2:10 pm!

Happening now at poster 107! Come say hi :)