3 citations · 4 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…
Improving realistic semi-supervised learning with doubly robust estimation
Khiem Pham, Charles Herrmann, Ramin Zabih
A major challenge in Semi-Supervised Learning (SSL) is the limited information available about the class distribution in the unlabeled data. In many real-world applications this ar…
Motion Prompting: Controlling Video Generation with Motion Trajectories
Daniel Geng, Charles Herrmann, Junhwa Hur +11
Motion control is crucial for generating expressive and compelling video content; however, most existing video generation models rely mainly on text prompts for control, which stru…
MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion
Junyi Zhang, Charles Herrmann, Junhwa Hur +5
Estimating geometry from dynamic scenes, where objects move and deform over time, remains a core challenge in computer vision. Current approaches often rely on multi-stage pipeline…
DreamWalk: Style Space Exploration using Diffusion Guidance
Michelle Shu, Charles Herrmann, Richard Strong Bowen +2
Text-conditioned diffusion models can generate impressive images, but fall short when it comes to fine-grained control. Unlike direct-editing tools like Photoshop, text conditioned…