Ameesh Makadia
79 posts
@kiamada
Research Scientist, Google Research, NYC. https://t.co/0I4eCVDeLu https://t.co/M7JUu05A4N
📢 Google's Project Starline is looking for Student Researchers! Our team seeks students interested in real-time, high-realism human 3D modeling, relighting, materials reconstruction, novel view synthesis, diffusion models, transformers, and more. 🧵Look below for details!
Our new paper, "Spectral Image Tokenizer", is on arXiv! We train a tokenizer on DWT coefficients that enables autoregressive coarse-to-fine image generation, w/ applications to multiscale text-to-image, and text-guided editing. w/ @kiamada, @msuhail153 arxiv.org/abs/2412.09607
📢📢📢 introducing 𝐩𝐢𝐱𝐞𝐥𝐒𝐩𝐥𝐚𝐭: feed-forward Gaussian splats from image pairs! pixelNeRF + 3D Gaussian Splatting == davidcharatan.com/pixelsplat Led by @DavidCharatan and @sizhe_lester_li, collaboration with @vincesitzmann
Looking forward to this! I'll talk about Neural Ideograms and Geometry-Grounded Representation Learning ... and cats and dogs and owls! Thank you @KostasPenn @CongyueD and co for organizing this! Excited to be part of the program!
Our Equivariant Vision workshop features five great speakers @erikjbekkers @HaggaiMaron @ninamiolane @_machc, and Leo Guibas, spotlight talks, posters, and a tutorial prepared for the vision audience. Come tomorrow, Tuesday, at 8:30am in Summit 321! Thank you @CongyueD for leading the organization! #schedule" target="_blank" rel="nofollow noopener">equivision.github.io/index.html#sch…
Excited to share AIM 🎯 - a set of large-scale vision models pre-trained solely using an autoregressive objective. We share the code & checkpoints of models up to 7B params, pre-trained for 1.2T patches (5B images) achieving 84% on ImageNet with a frozen trunk. (1/n) 🧵
Applying computer vision models designed for planar images to data projected on spherical surfaces is challenging. Here we present an open-source library in JAX to solve the challenges of rotation and regular sampling for state-of-the-art performance → goo.gle/46z3vD7
Applying computer vision models designed for planar images to data projected on spherical surfaces is challenging. Here we present an open-source library in JAX to solve the challenges of rotation and regular sampling for state-of-the-art performance → goo.gle/46z3vD7
