Google announces PALP Prompt Aligned Personalization of Text-to-Image Models paper page: huggingface.co/papers/2401.06… Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally,

#MDM is now 40X faster 🤩🤩🤩 (~0.4 sec/sample) How come?!? (1) We released the 50 diffusion steps model (instead of 1000 steps) which runs 20X faster. (2) Calling CLIP just once and caching the result runs 2X faster for all models. github.com/GuyTevet/motio…

“Monkey See, Monkey Do”! 🐵 A cool new work demonstrating how manipulating self-attention features in diffusion models enable zero-shot motion transfer. It can generate motions that follow a leader dancer in various motifs, including those of a gorilla! monkeyseedocg.github.io

We are happy to share Mismatch Quest acceptance to #ECCV2024 European Conference on Computer Vision #ECCV2026 ! 🥳 Check out additional details in the project website mismatch-quest.github.io Congrats to the team Yonatan Bitton yoni shafir, Roopal Garg, Xi Chen, Dani Lischinski , Daniel Cohen-Or, Idan Szpektor

Can you answer these riddles? We are happy to present our new paper “Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models”. Paper: Website: visual-riddles.github.io 🧵

Sorteo el 18 de septiembre. Camiseta de San Lorenzo titular original XL. Para participar: 📌Nos tenés que seguir y darle rt a este tweet. 📌Seguir a BoedoEnMi. 📌Si querés tener más chances seguinos en nuestro canal de Youtube. 👇 youtube.com/@sanlorenzored…

Checkout our work "GameNGen". A Gaming engine powered by a diffusion-model that simulates DOOM in Real-Time! Find out more: gamengen.github.io Amazing effort and fun collaboration with the incredible Dani Valevski, Yaniv Leviathan, and Shlomi Fruchter!

🚀 Meet DiP: our newest text-to-motion diffusion model! ✨ Ultra-fast generation ♾️ Creates endless, dynamic motions 🔄 Seamlessly switch prompts on the fly Best of all, it's now available in the MDM codebase: github.com/GuyTevet/motio… [1/3]

Excited to introduce our new work: ImageRAG 🖼️✨ rotem-shalev.github.io/ImageRAG We enhance off-the-shelf generative models with Retrieval-Augmented Generation (RAG) for unknown concept generation, using a VLM-based approach that’s easy to integrate with new & existing models! [1/3]

Thrilled to see this plot in a recent survey on 'personalized image generation' (arxiv.org/abs/2502.13081) — highlighting the impact of our work! Huge congratulations to my fantastic students, whose creativity and dedication continue to drive exciting advances in the field!

🚀 New preprint! 🚀 Check out AnyTop 🤩 ✅ A diffusion model that generates motion for arbitrary skeletons 🦴 ✅ Using only a skeletal structure as input ✅ Learns semantic correspondences across diverse skeletons 🦅🐒🪲 🔗 Arxiv: arxiv.org/abs/2502.17327

🔔Excited to announce that #AnyTop has been accepted to #SIGGRAPH2025!🥳 ✅ A diffusion model that generates motion for arbitrary skeletons ✅ Using only a skeletal structure as input ✅ Learns semantic correspondences across diverse skeletons 🌐 Project: anytop2025.github.io/Anytop-page

And finally, our work Unblocking Fine-Grained Caption Evaluation: AutoRater & Critic-and-Revise (lnkd.in/df94d8eD) – led by Brian Gordon