🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Meta presents EMU VIDEO: Factorizing Text-to-Video Generation by Explicit Image Conditioning blog: emu-video.metademolab.com paper: emu-video.metademolab.com/assets/emu_vid… present EMU VIDEO, a text-to-video generation model that factorizes the generation into two steps: first generating an image

To support future research for instruction-based image editing, we publicly release a diverse and challenging benchmark that includes 7 different image editing tasks. Link to the benchmark: huggingface.co/datasets/faceb… And here is an example of edits on my own image😇

Thanks for sharing our work! joint work with Omri Puny, Itai Gat, Brian Karrer, Uriel Singer and Yaron Lipman

Thank you AK for sharing our recent work, Emu Video Edit, on video editing! Project page: fdd-video-edit.github.io An amazing collaboration with Amit Zohar, Yuval Kirstain, Shelly Sheynin, Adam Polyak, Devi Parikh, and Yaniv Taigman

Excited to share our recent work! 🎥📝 We propose an unsupervised method that achieves a new state-of-the-art in text-based video editing 🚀 Check it out: fdd-video-edit.github.io W/ the amazing Uriel Singer, Yuval Kirstain, Shelly Sheynin, Adam Polyak, Devi Parikh, and Yaniv Taigman

Thrilled to share that our paper has been accepted to #ECCV2024! 🚀🚀🎉

Thrilled to announce that our paper has been accepted for an Oral presentation at #ECCV2024! See you in Milan! With Uriel Singer, Yuval Kirstain, Shelly Sheynin, Adam Polyak, Devi Parikh, and Yaniv Taigman

Introducing✨Still-Moving✨—our work from Google DeepMind that lets you apply *any* image customization method to video models🎥 Personalization (DreamBooth)🐶stylization (StyleDrop) 🎨 ControlNet🖼️—ALL in one method! Plus… you can control the amount of generated motion🏃‍♀️ 🧵👇

Like text-to-video, but hate the hallucinations? This paper is the answer you are looking for!

We are presenting 3 orals and 1 spotlight at #ICLR2025 on two primary topics: On generalizing the data-driven flow matching algorithm to jump processes, arbitrary discrete corruption processes, and beyond. And on highly scalable algorithms for reward-driven learning settings.

The longer reasoning LLM thinks - the more likely to be correct, right? Apparently not. Presenting our paper: “Don’t Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning”. Link: arxiv.org/abs/2505.17813 1/n

Beyond excited to share FlowMo! We found that the latent representations by video models implicitly encode motion information, and can guide the model toward coherent motion at inference time Very proud of ariel shaulov Itay Hazan for this work! Plus, it’s open source! 🥳

Exciting news from #ICML2025 & #ICCV2025 🥳 - 🥇 VideoJAM accepted as *oral* at #ICML2025 (top 1%) - Two talks at #ICCV2025 ☝️interpretability in the generative era ✌️video customization - Organizing two #ICCV2025 workshops ☝️structural priors for vision ✌️long video gen 🧵👇

Excited to share our recent work on corrector sampling in language models! A new sampling method that mitigates error accumulation by iteratively revisiting tokens in a window of previously generated text. With: Neta Shaul Uriel Singer Yaron Lipman Link: arxiv.org/abs/2506.06215

**Transition Matching** is a new iterative generative paradigm using Flow Matching or AR models to transition between generation intermediate states, leading to an improved generation quality and speed!

Check out our team's latest work, led by Uriel Singer and Neta Shaul!

Introducing Transition Matching (TM) — a new generative paradigm that unifies Flow Matching and autoregressive models into one framework, boosting both quality and speed! Thank you for the great collaboration Neta Shaul Itai Gat Yaron Lipman

This paper is awesome. 🔥 Flow-matching for flow-matching! ❌No more coarse-to-fine generation. 🚀Coarse and fine details emerge together during generation. 🏆Results look super promising, especially when you see how the images evolve.

Difference Transition Matching (DTM) process is so simple to Illustrate, you can calculate it on a whiteboard! At each step: Draw all lines connecting source and target (shaded) ⬇️ List those intersecting with the current state (yellow) ⬇️ Sample a line from the list (green)

If you're curious to dive deeper into Transition Matching (TM)✨🔍, a great starting point is understanding the similarities and differences between 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧 𝐌𝐚𝐭𝐜𝐡𝐢𝐧𝐠 (𝐃𝐓𝐌) and Flow Matching (FM)💡.