Marigold V2 is out! (to appear at SIGGRAPH Asia 2026)
If you missed V1: Marigold post-trains an image generator into a depth estimator on one GPU -- the accessible research game.
V2 upgrades to a diffusion transformer: produces very sharp edges, and is quite versatile!
Yay, finally! Introducing Vision Banana🍌 from @GoogleDeepMind, our unified model that outperforms SoTA specialist models on various vision tasks!
By treating 2D/3D vision tasks as image generation, we unlock a new foundation for CV.
Project page: https://t.co/GQgRi6mWwC
(1/5)
Continuous diffusion had a good run—now it’s time for Discrete diffusion!
Introducing Anchored Posterior Sampling (APS)
APS outperforms discrete and continuous baselines in terms of performance & scaling on inverse problems, stylization, and text-guided editing.
Call for PhD research interns at Google:
Conduct research on Generative AI (Diffusion Models, Large Language Models) for commerce.
Deadline: Feb 15
Details are in the application form: https://t.co/hbhGWr4QGl
Thanks to @unixpickle for noting that we missed out on other inpainting diffusion works in our preprint. We corrected that and apologize for this mistake.
Inpainting with Diffusion Models - CVPR 2022
Inpainting approaches using unconditional diffusion model often suffer boundary artifacts.
Our inference schedule alleviates this by sampling every iteration multiple times.
Code + Models + Paper: https://t.co/Pno4odWWqV
The blue regions are unknown and get inpainted by RePaint. The video shows each step of the inference. The pulsation comes from redoing the denoising.
Details: https://t.co/Pno4odWWqV
Thanks to my coauthors @MDanelljan, @afromero, @DrFisherYu, Radu Timofte and Luc Van Gool!
We are organizing a Challenge for Image Inpainting in the NTIRE 2022 Workshop that will be held on June in conjunction with CVPR 2022. Two cool things: a lot of different and challenging masks, and inpainting towards art creation. Repo: https://t.co/gmh9H3M0F7.
📯 ICCV 2021 Oral: "Warp Consistency for Unsupervised Learning of Dense Correspondences", with @MDanelljan, @DrFisherYu and Luc Van Gool.
Join us Session 8, 13th 9AM and 15th 4PM (EDT) if you want to know more!
paper: https://t.co/FAOPl9MIg3
video: https://t.co/N3AZq9XHoC
Our #ICCV2021 paper on Adaptive Task-Relational Context, leveraging neural architecture search and attention mechanisms in the multi-task learning setting, is now paired with source code! @menelaoskanakis@stam_g
paper: https://t.co/JQDOGiT2YV
code: https://t.co/JrlNmmrThx
Introducing Generative Scene Networks (GSN), a generative model for learning radiance fields for realistic scenes. With GSN we can sample scenes from the learned prior and move through them with a freely moving camera.
Arxiv: https://t.co/rYiFH4uhLp
Scenes sampled from the prior:
Our paper "Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather" has been accepted to ICCV 2021! Our preprint is available at https://t.co/ES0CYRaQZM. Code available at https://t.co/MylYEWbKi6.
Code released for our #CVPR2021 oral paper "DeFlow": A method for unpaired learning of image-to-image translation with normalizing flows.
Excellent master's thesis by @va_wolf👏
Also with @AndreasLugmayr et al.
arXiv: https://t.co/Owzw5qK8ye
code: https://t.co/RiY7HzwlDA
Looking for dense and trustworthy correspondences?
Check out our #CVPR2021 oral paper w/ @MDanelljan:
Learning Accurate Dense Correspondences and When to Trust Them
Arxiv: https://t.co/80FFMo904w
Code: https://t.co/G1C3nc4PXw
Project page: https://t.co/Ee27975YrM
Thanks to @deviparikh for this handy review organization spreadsheet and the guide on how to use it.
[Spreadsheet] https://t.co/nPsurlG5ZM
[Guide] https://t.co/U8IPdvZcKo