Excited to share our #ICCV2025 work Reusing Computation in Text-to-Image Diffusion for Efficient Generation of Image Sets!

Our method generates large sets of images using significantly less compute than standard diffusion.

📎https://ddecatur.github.io/hierarchical-diffusion/

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After a prolific and rewarding decade at Adobe Research (see vova.kim), I’ve taken on a new role as Director of Applied AI/ML at Adobe. I’m building a new team and we’re looking for outstanding Applied Scientists (mostly for visual data). Shoot me an e-mail or pass along!

A remarkable thing about human intelligence, is that a single-celled slime, driven purely by natural selection, somehow turned into a hairless ape with a seemingly useless adaptation which allowed it to build generalizable abstract representations of the natural world. AI, on the other hand..

Yes!

🎓 We introduce SAMa! A material selection and segmentation model on 3D models in any format (3DGS, NeRF, Mesh).
Given a user click, we propose to select all regions on an objects with the same material. We can also do segmentation in under a minute: mfischer-ucl.github.io/sama/

🌟 Text-to-3D is awesome ! But how do we iterate on the generated 3D model, to get just the right result? Do we tweak the prompt endlessly? Revert to traditional 3D modeling techniques?
We propose a solution to "3D inpainting” 🤩🎨

Project: amirbarda.github.io/Instant3dit....
A thread. 🧵 1/