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GenAI Media Generation Challenge Workshop #CVPR2025 is today (6/17): 📍 Summit 423-425 ⌛️ 1:15 - 5:40 pm ⌛️ We have exciting keynote speeches from Jun-Yan Zhu (Jun-Yan Zhu), Sergey Tulyakov, Richard Zhang (Richard Zhang), Yuanzhen Li, and Tim Salimans to share the latest progress on

Happening soon! Following talks from Jun-Yan Zhu and Sergey Tulyakov. Summit 423-425 in the GenAI Media Generation workshop: gamgc.github.io

Stop by our #CVPR2025 posters to meet the team! We present 7 poster today, 2 papers are highlights. Video generation, 3D scene generation, 4D generation, improving quality of synthesized images and more!

Snap presents VIMI Grounding Video Generation through Multi-modal Instruction Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datasets, resulting in a

At Creative Vision, Snap Research, we’re excited to announce our 2025 call for interns. Our past interns have built some of the world’s best image, video, and 3D generators. We’ve introduced new personalization methods and developed the world’s fastest mobile foundational image

Future of video generation and editing! Videos differ from images in that they have an additional dimension - time. We know about curse of dimensionality, but in this case it's a blessing! It unlocks so many new ways to be creative!

Where are good old progressive diffusion models? 🤔 Breaking generation to multiple resolution scales is a great idea, but complexity (multiple models, custom diffusion process, etc) stalled scaling. Our Decomposable Flow Matching packs multi-scale perks into one scalable model.

Why is progressive generation so complex? 🤔 It doesn't have to be. Our Decomposable Flow Matching (DFM) simplifies the process into a single, straightforward flow model, 🚀 beating prior work in image and video synthesis. #AI #Research #MachineLearning