89 citations · 181 across the 8 of their papers we have counts for
8 papers
FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
Black Forest Labs, Stephen Batifol, Andreas Blattmann +18
We present evaluation results for FLUX.1 Kontext, a generative flow matching model that unifies image generation and editing. The model generates novel output views by incorporatin…
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann +14
Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perc…
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
Andreas Blattmann, Tim Dockhorn, Sumith Kulal +9
We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion mod…
Putting People in Their Place: Affordance-Aware Human Insertion into Scenes
Sumith Kulal, Tim Brooks, Alex Aiken +5
We study the problem of inferring scene affordances by presenting a method for realistically inserting people into scenes. Given a scene image with a marked region and an image of…
Programmatic Concept Learning for Human Motion Description and Synthesis
Sumith Kulal, Jiayuan Mao, Alex Aiken +1
We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low-level motion and high-level description as motion concepts.…
Hierarchical Motion Understanding via Motion Programs
Sumith Kulal, Jiayuan Mao, Alex Aiken +1
Current approaches to video analysis of human motion focus on raw pixels or keypoints as the basic units of reasoning. We posit that adding higher-level motion primitives, which ca…