12 citations · 15 across the 5 of their papers we have counts for
5 papers
MotionV2V: Editing Motion in a Video
Ryan Burgert, Charles Herrmann, Forrester Cole +4
While generative video models have achieved remarkable fidelity and consistency, applying these capabilities to video editing remains a complex challenge. Recent research has explo…
LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
Xiang Li, Cristina Mata, Jongwoo Park +8
Vision Language Models (VLMs) have recently been leveraged to generate robotic actions, forming Vision-Language-Action (VLA) models. However, directly adapting a pretrained VLM for…
Diffusion Illusions: Hiding Images in Plain Sight
Ryan Burgert, Xiang Li, Abe Leite +2
We explore the problem of computationally generating special `prime' images that produce optical illusions when physically arranged and viewed in a certain way. First, we propose a…
Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors
Ryan Burgert, Kanchana Ranasinghe, Xiang Li +1
Recently, text-to-image diffusion models have shown remarkable capabilities in creating realistic images from natural language prompts. However, few works have explored using these…
Neural Neural Textures Make Sim2Real Consistent
Ryan Burgert, Jinghuan Shang, Xiang Li +1
Unpaired image translation algorithms can be used for sim2real tasks, but many fail to generate temporally consistent results. We present a new approach that combines differentiabl…