Shelly Sheynin

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Shelly Sheynin

Shelly Sheynin

@ShellySheynin

Research Scientist @AIatMeta; Working on Media Generation; Meta Movie Gen, Emu Edit, Make-a-Video 3D, KNN Diffusion, Make-A-Scene

Katılım Eylül 2020
209 Takip Edilen1.1K Takipçiler
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Shelly Sheynin
Shelly Sheynin@ShellySheynin·
I’m thrilled and proud to share our model, Movie Gen, that we've been working on for the past year, and in particular, Movie Gen Edit, for precise video editing. 😍 Look how Movie Gen edited my video!
AI at Meta@AIatMeta

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ go.fb.me/kx1nqm 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

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Ofir Magdaci
Ofir Magdaci@Magdaci·
🧵 Forecasting Next Season’s FIFA Player Ratings with ML 1/ Imagine predicting the 2026 FIFA ratings… on the same day the 2025 edition drops. You’ve seen everything up to now — but none of what comes next. Could you forecast who’ll rise? Could machine learning?
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Hila Chefer
Hila Chefer@hila_chefer·
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 🧵👇
Hila Chefer@hila_chefer

VideoJAM is our new framework for improved motion generation from @AIatMeta We show that video generators struggle with motion because the training objective favors appearance over dynamics. VideoJAM directly adresses this **without any extra data or scaling** 👇🧵

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Min Choi
Min Choi@minchoi·
Meta introduces VideoJAM This AI makes video animations smoother and more realistic by improving how motion is generated. 10 wild examples: 1. Fingers press into a shimmering slime ball.
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Lucas Beyer (bl16)
Lucas Beyer (bl16)@giffmana·
This is extremely cool! They find diffusion loss is not very sensitive to motion. Thus they fine-tune videogen models with additional explicit motion prediction, making the model generate much more coherent videos. Also, Hila has been doing consistently good work, follow her!
Hila Chefer@hila_chefer

VideoJAM is our new framework for improved motion generation from @AIatMeta We show that video generators struggle with motion because the training objective favors appearance over dynamics. VideoJAM directly adresses this **without any extra data or scaling** 👇🧵

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el.cine
el.cine@EHuanglu·
wow.. Meta AI just dropped new AI model, the motion is incredible and.. it has a great understanding of physics 15 examples:
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AK
AK@_akhaliq·
Meta just dropped VideoJAM Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models comparison with openai sora and kling
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Shelly Sheynin
Shelly Sheynin@ShellySheynin·
Super excited to share our recent work, VideoJAM, for motion and physics improvement in T2V models. VideoJAM sets new SOTA in motion generation and understanding.🥳 Project Page: hila-chefer.github.io/videojam-paper…
Hila Chefer@hila_chefer

VideoJAM is our new framework for improved motion generation from @AIatMeta We show that video generators struggle with motion because the training objective favors appearance over dynamics. VideoJAM directly adresses this **without any extra data or scaling** 👇🧵

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Shelly Sheynin
Shelly Sheynin@ShellySheynin·
@YVinker MIT is so lucky to have you! Congratulations ❤️
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Yael Vinker🎗
Yael Vinker🎗@YVinker·
Excited to share that I officially completed my PhD in Computer Science at Tel Aviv University and have joined Prof. Antonio Torralba’s lab at MIT for my postdoc! Looking forward to researching visual communication, and surviving Boston winters without turning into a popsicle!❄️
Yael Vinker🎗 tweet media
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Guy Yariv
Guy Yariv@guy_yariv·
[1/8] Recent work has shown impressive Image-to-Video (I2V) generation results. However, accurately articulating multiple interacting objects and complex motions remains challenging. In our new work, we take a step toward addressing this challenge.
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Bilawal Sidhu
Bilawal Sidhu@bilawalsidhu·
Ok finally dug into Meta's new Movie Gen paper. Text-to-video is cool and all but, to me the precise editing feature is the game changer. I mean just look at these results 🤯 It can handle complex VFX tasks like replacing environments, doing set extensions, swapping characters, removing items, adding particle effects with realistic lighting interaction. The coolest bit to me is how they trained this model, because paired before/after vfx editing datasets are super scarce. TL;DR They taught it video editing through a clever three-stage process: 1. Started with image editing data, treating it like single-frame video edits. 2. Created synthetic video editing tasks by animating still image edits and using AI models (like SAM and DINO) for object segmentation. 3. The model generated edited videos, and then learned to reconstruct the originals from the edited version Meta calls this "video editing via backtranslation" and the results speak for themselves.
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Ahmad Al-Dahle
Ahmad Al-Dahle@Ahmad_Al_Dahle·
I couldn’t be more excited to share our latest AI research breakthrough. We call it Meta Movie Gen and it’s a collection of state-of-the-art models that combine to deliver the most advanced video generation capability ever created. Check it out: ai.meta.com/research/movie…
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Joelle Pineau
Joelle Pineau@jpineau1·
Sharing some of our latest work on generative AI! The video editing features and sound generation are especially exciting. And it comes with a full research paper.
AI at Meta@AIatMeta

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ go.fb.me/kx1nqm 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

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Amit Zohar
Amit Zohar@amit_zhr·
Thrilled to share results from the Movie Gen models we've been working on these past few months, and particularly the Movie Gen Edit model for precise editing! 🚀🚀
AI at Meta@AIatMeta

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ go.fb.me/kx1nqm 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

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Yuval Kirstain
Yuval Kirstain@YKirstain·
So proud to be part of the Movie Gen project, pushing GenAI boundaries! Two key insights: 1. Amazing team + high-quality data + clean, scalable code + general architecture + GPUs go brr = SOTA video generation. 2. Video editing *without* supervised data: train a *single* model for image editing and video generation → pseudo video editing data = SOTA video editing.
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Nir Ben-Zvi
Nir Ben-Zvi@nir_benz·
מטא שחררו את Meta Movie Gen - ״עבודה״ (יותר כמו stack אלגוריתמי מורכב של הרבה חלקים) ליצירת תוכן; text2video, video editing, img2video ועוד מלא. אתעמק בזה בהמשך אבל בנתיים פרגון לקבוצה הישראלית שמככבת (@amit_zhr תייג את האחרים :P ).
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Adam Polyak
Adam Polyak@adam_polyak90·
Excited to share our progress on Movie Gen, a SOTA model for video generation! 🎥✨ I worked on this project as part of a cutting-edge team 🔥, pushing the boundaries of video editing ✂️— all without supervised data. Can’t wait to show you what’s next! 🚀🎬
AI at Meta@AIatMeta

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ go.fb.me/kx1nqm 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

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