Our new method, Terminal Velocity Matching (TVM), works on large-scale models while enjoying all the advantages of IMM — training stability, high-quality samples, efficient inference. This moves us closer to a genuine evolutionary step beyond today’s diffusion models.
Introducing Terminal Velocity Matching: a scalable, single-stage generative training method that delivers diffusion-level quality with a 25× fewer inference steps, now trained at 10B+ scale.
lumalabs.ai/blog/engineeri…
Introducing Ray2, a new frontier in video generative models. Scaled to 10x compute, #Ray2 creates realistic videos with natural and coherent motion, unlocking new freedoms of creative expression and visual storytelling. Available now. Learn more lumalabs.ai/ray.
Introducing the all-new Luma Photon text-to-image models, now available in the Luma API. Photon and Photon Flash are the most creative, personalizable, and intelligent visual models for creatives, bringing a step-function change in the cost of high-quality image generation.
Say hello to the all-new #DreamMachine. 🚀 Your visual thought partner—where ideas become reality. Ideate, visualize, and share your ideas with the world. Available now.
🚀 Welcome to the era of Hyperfast video generation: with 10x faster inference, you can now generate full-quality Dream Machine v1.6 clips in under 20 seconds. No "turbo" or "distilled" models - just uncompromised quality. Available today to all subscribers and API customers.
🚀 Introducing the Dream Machine API. Developers can now build and scale creative products with the world's most popular and intuitive video generation model without building complex tools in their apps. Start today lumalabs.ai/dream-machine/…#LumaDreamMachine
Are you annoyed by popping artifacts with 3DGS?
Check out our new Siggraph paper StopThePop. StopThePop adds per-pixel sorting with minimal performance impact.
🚀 Excited to announce our latest research: “StopThePop: Sorted Gaussian Splatting for View-Consistent Real-Time Rendering”, which will appear in #SIGGRAPH2024! 🎉
Finally, fast and view-consistent rendering of 3D Gaussians, without popping artifacts! r4dl.github.io/StopThePop/
We’re excited to introduce our group's new research paper, “Collaborative Control for Normal-Conditioned PBR Image Generation”, in which we tackle high quality single-view PBR materials! 🧵
arxiv: arxiv.org/abs/2402.05919
project page (with live demo!): unity-research.github.io/holo-gen/
Excited to announce our #ECCV2022 paper "AdaNeRF"! Together with @LvZhaoyang and @MZollhoefer , we show that our adaptive, fully neural representation can compete with (and even outperform) modern hybrid approaches in terms of quality, compactness and rendering speed. 1/4
@DaoudiMed64@CVPR@DaoudiMed64 same problem here. I think it will only get worse, because soon the first session starts and even more people will have that google docs open at the same time to find the posters they are looking for.
Happy to introduce DeltaCNN (#CVPR2022), a CNN framework for sparse video inference. By reusing previous results for unchanged pixels, we accelerate CNN inference up to 7x compared to cuDNN.
Project page: dabeschte.github.io/DeltaCNN/
@Lane_Kerr Thanks. But unfortunately we only had few active players in the last years and the maintenance cost too much time and money to keep Splitter alive.
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2K Followers 545 FollowingAutonomy @Rivian / frml CEO and Cofounder @3rddimensionai (acquired, Oct '26) / ex @tesla @activision @ea / Made @callofduty 🇹🇷 🇺🇸
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9K Followers 1K FollowingNews about radiance fields, including Gaussian Splatting & Neural Radiance Fields (NeRFs).
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1K Followers 264 FollowingI’m the Co-Head of Visual Research at Stability AI with research interests in the intersection of machine learning and computer graphics
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