Excited to share that Inspiration Seeds has received an Honorable Mention award this year at SIGGRAPH! 🎉
👉https://t.co/gE9MRvbKcV
Huge thanks and congrats to the best team! @kfir99@EladRichardson
Looking forward to seeing you in LA in July!🌱
Even today, with powerful image editing models, making fine-grained structural changes to 3D shapes remains a major challenge.
In our new #SIGGRAPH2026 paper, Prox-E, we use primitive-based abstraction to leverage VLMs for precise, reasoning-based 3D editing!
👇
Excited to share our work accepted to #SIGGRAPH2026 ! Video generation models struggle with something few talk about: their transformations don't evolve smoothly. You get long boring stretches... then a sudden semantic jump where everything "catches up" at once.
1/7
When rewards conflict, what should RL post-training of diffusion models optimize?
In visual generation, objectives are often in tension:
Prompt adherence can conflict with source preservation.
Photorealism can conflict with stylization.
In our new paper, ParetoSlider, we introduce a multi-objective RL framework that trains a single diffusion model for continuous control over competing reward objectives 🧵
The FAIR Montréal office is expanding!
We're looking for folks to join us who are exciting about world modelling. Hiring for both recent grads as well as more experienced researchers.
Feel free to reach out, but please read the posting first :)
https://t.co/6yZUyej4VZ
3D editing has long relied on workarounds: per-asset optimization, 2D view propagation, or hacking frozen priors. The bitter lesson is the same one image editing already learned. Train a native model, end-to-end.
Introducing ShapeUP, accepted to SIGGRAPH 2026 💫
I am *very* excited to announce our SIGGRAPH 2026 workshop:
Lines & Minds: Visual Abstraction in Art, Psychology, and Computer Graphics 🎨🧠🫖
🔗 https://t.co/dZaoDPv5Zt
📅 Sunday, July 19
Join us to explore how visual abstraction shapes how we think, create, and communicate.
Video models as Physics simulators. 🌍🎥
[1/] In our latest work, WinDiNet, we finetuned a pre-trained video model into a differentiable physics engine. 1000x faster than traditional CFD solvers.
Project page: https://t.co/LAx7t00y3e
Abs: https://t.co/OdcgbKeQEG
[4/] Shout out to Janne Perini and Rafael Bischof for their hard work, and the great team: Ayça Duran, Michael A. Kraus, Siddhartha Mishra, and @bernd_bickel.
Video models as Physics simulators. 🌍🎥
[1/] In our latest work, WinDiNet, we finetuned a pre-trained video model into a differentiable physics engine. 1000x faster than traditional CFD solvers.
Project page: https://t.co/LAx7t00y3e
Abs: https://t.co/OdcgbKeQEG
[3/] We fine-tuned LTX with 10k 2D urban wind flow simulations by (i) fine-tuning the VAE decoder with physics-informed losses, and (ii) replacing the text encoder with a wind speed and direction embedder.
Creative work often starts before we can describe what we're looking for. What role can generative models play at this stage?
🌱Our new work, Inspiration Seeds, reveals hidden visual connections between images, creating a purely visual exploration space.
🔗https://t.co/gE9MRvbKcV
Check out our most recent work on the training-free video editing from @GoogleDeepMind. One of the cool things about the approach is that it allows modifying the motions of particular scene elements while keeping the dynamics of other parts (check the 🎱 example!). The method is model-agnostic https://t.co/McaTNHmTT4