📢 Announcing MoA !! TL;DR - We introduce Mixture-of-Attention (MoA) a new architecture for personalization of generative models that disentangles the generation of given subjects and the context from the prior. snap-research.github.io/mixture-of-att… #snap #GenerativeAI

Snap presents MoA Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation We introduce a new architecture for personalization of text-to-image diffusion models, coined Mixture-of-Attention (MoA). Inspired by the Mixture-of-Experts

The progress of the Animatediff community over the past 10 months has been miraculous - see attached! Now, closed startups like Krea are taking the fruits of all this effort - so I'd like to tell the story of how we got here & what people who believe in open source can do.

GTR Improving Large 3D Reconstruction Models through Geometry and Texture Refinement We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models like LRM that use a transformer-

As a speech/audio researcher, I think it’s a big breakthrough in advancing *human-level audio generation*. Why? Because this is NOT just a video-to-audio model that generates what it sees in the physical world... but a model that learns to *DESIGN* sounds like a human🤯 Here

🌟 Snap Research Internship🌟 The creative vision team is looking for 2025 interns! If you are a PhD student with passion in 3D/4D, video gen, personalization, or efficiency, reach out to via the application link or emails! 🔥Website: snap-research.github.io/cv-call-for-in…

🔥DELTA: Dense Efficient Long-range 3D Tracking for any video DELTA can efficiently (10x faster!) track EVERY pixel in 3D space from monocular videos. Please check out our project page and paper for more details and samples! 👑snap-research.github.io/DELTA/

A common question nowadays: Which is better, diffusion or flow matching? 🤔 Our answer: They’re two sides of the same coin. We wrote a blog post to show how diffusion models and Gaussian flow matching are equivalent. That’s great: It means you can use them interchangeably.

Please check out our latest 3D shape editing framework, PrEditor3D! PrEditor3D can precisely edit only the intended regions, and it’s fast!

Our 4Real poster session is at NeurIPS Conference tomorrow, from 11 am to 2 pm. 🎉 Swing by and say hi if you're around! Also, don’t miss our follow-up work, 4Real-Video, at snap-research.github.io/4Real-Video/ 🚀!

🚀🚀🚀Omni-ID: Holistic Identity Representation Designed for Generative Tasks snap-research.github.io/Omni-ID/ Excited to share Omni-ID, a novel facial representation tailored for generative tasks! It captures diverse expressions & poses, enabling high-fidelity personalized generation.

Video-to-Audio and Audio-to-Video models struggle with temporal alignment. AV-Link solves the problem by conditioning on diffusion model features Great collaboration with Moayed Haji Ali , Aliaksandr Siarohin , Ivan Skorokhodov , Alper Canberk , Kwot Sin Lee, Vicente Ordonez and Sergey Tulyakov

The Physical Turing Test: your house is a complete mess after a Sunday hackathon. On Monday night, you come home to an immaculate living room and a candlelight dinner. And you couldn't tell whether a human or a machine had been there. Deceptively simple, insanely hard. It is the

Presenting PrEditor3D at #CVPR2025 📢📢 If you'd like to learn more about our work and discuss 3D generation/editing, come visit our poster on Friday, June 13th, in ExHall D between 10:30-12:30 (Poster #44). Project Page: ziyaerkoc.com/preditor3d