Excited to announce a new track of accelerating Generative AI: pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation https://t.co/6ro55E1XGP Distill 20B flow models now using just an L2 loss via imitation learning for SOTA diversity and teacher-aligned quality.

Woohoo! Imagine, Verify, Execute (IVE) is accepted to CoRL 2025! 🎉 Congrats to the incredible @umdcs students Seungjae Lee @JayLEE_0301, Daniel Ekpo (@daniekpo7), Haowen Liu!

We will present FlexTok at #ICML2025 on Tuesday! Drop by to chat with @JRAllardice and me if you're interested in tokenization, flexible ways to encode images, and generative modeling. 📆 Tue, Jul 15, 16:30 PDT 📍 East Exhibition Hall, Poster E-3010 🌐

Which multimodal LLM should you be using to edit graphics in Blender? Today, we’re releasing our #CVPR2025 Highlight🌟 work, #BlenderGym 🏋️‍♀️, the first agentic 3D graphics editing benchmark that will tell you exactly how multimodal LLMs compare in their Blender-editing skills.

Excited to share our work: Gaussian Mixture Flow Matching Models (GMFlow) https://t.co/XWAy2VCJlg GMFlow generalizes diffusion models by predicting Gaussian mixture denoising distributions, enabling precise few-step sampling and high-quality generation.

Happy to share that we released FlexTok code and models on https://t.co/2pRCUcMbQn. Try them with our interactive @huggingface demo on

🏡Building realistic 3D scenes just got smarter! Introducing our #CVPR2025 work, 🔥FirePlace, a framework that enables Multimodal LLMs to automatically generate realistic and geometrically valid placements for objects into complex 3D scenes. How does it work?🧵👇

Meet Gemini Robotics: our latest AI models designed for a new generation of helpful robots. 🤖 Based on Gemini 2.0, they bring capabilities such as better reasoning, interactivity, dexterity and generalization into the physical world. 🧵 https://t.co/EXRJrmxGxl

In the past, we extended the convolution operator to go from low-level image processing to high-level visual reasoning. Can we also extend physical operators for more high-level physical reasoning? Introducing the Denoising Hamiltonian Network (DHN): https://t.co/GY76QreRge

Thrilled to announce that SG-I2V has been accepted at #ICLR2025 ! Huge thanks to the collaborators, reviewers, and ACs. Looking forward to presenting this in Singapore!

Congratulations to @UofTCompSci undergrads Helen Li, Junru Lin, Leo Tenenbaum and Sarah Walker who have received honourable mentions in the @CRAtweets 2024-2025 Outstanding Undergraduate Researcher Award program!  https://t.co/5jorFZ3Ze2

🔥 Introducing MVLift: Generate realistic 3D motion without any 3D training data - just using 2D poses from monocular videos! Applicable to human motion, human-object interaction & animal motion. Joint work w/ @jiajunwu_cs & Karen 💡 How? We reformulate 3D motion estimation as

Introducing 🧢CAP4D🧢 CAP4D turns any number of reference images (single, few, and many) into controllable real-time 4D avatars. 🧵⬇️ Website: https://t.co/l6hRa5jquQ Paper: https://t.co/fxGGu3X3cz

Do large multimodal models understand how to make dresses for your winter holiday party💃? We introduce AIpparel, a vision-language-garment model capable of generating and editing simulation-ready sewing patterns from text and images. Project page at https://t.co/DpIs2oZKkd.

[Hiring!] I am hiring multiple PhDs @CSatUSC @USCViterbi for this cycle. If you're interested in scene representations, neural simulation, generative AI, and robotics, feel free to mention my name in your application (no need to email). For USC masters/undergrads who're

Sharing something exciting we've been working on as a Thanksgiving gift: Diffusion Self-Distillation (DSD), which redefines zero-shot customized image generation using FLUX. DSD is like DreamBooth, but zero-shot/training-free. It works across any input subject and desired

📢 Excited to share our new work: AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers https://t.co/piUgl0MwjV We analyze what pre-trained video diffusion transformers understand about 3D and demonstrate dynamic scene generation with 3D control.

I'm recruiting graduate students for Fall 2025 to work at the intersection of Computer Vision, Deep Learning, and Robotics. If you are interested in building a controllable organic simulation engine and enabling safe robot learning, consider applying to UofT's CS PhD program 1/n

Check out our new paper in feed-forward 3DGS model for large scenes! And the code is also available

Symmetries are everywhere — from butterfly’s wings to Greek temples. But detecting them in noisy data? That’s a challenge. 🦋🏛 Our #SIGGRAPHAsia2024 paper, Robust Symmetry Detection via Riemannian Langevin Dynamics, tackles this: https://t.co/zVkv4LZv6N 🧵(1/n)