17 citations · 45 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
Lijie Fan, Tianhong Li, Siyang Qin +6
Scaling up autoregressive models in vision has not proven as beneficial as in large language models. In this work, we investigate this scaling problem in the context of text-to-ima…
High-Resolution Frame Interpolation with Patch-based Cascaded Diffusion
Junhwa Hur, Charles Herrmann, Saurabh Saxena +6
Despite the recent progress, existing frame interpolation methods still struggle with processing extremely high resolution input and handling challenging cases such as repetitive t…
A Simple Approach to Unifying Diffusion-based Conditional Generation
Xirui Li, Charles Herrmann, Kelvin C. K. Chan +4
Recent progress in image generation has sparked research into controlling these models through condition signals, with various methods addressing specific challenges in conditional…
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…
Lumiere: A Space-Time Diffusion Model for Video Generation
Omer Bar-Tal, Hila Chefer, Omer Tov +14
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthes…
Efficient Hybrid Zoom using Camera Fusion on Mobile Phones
Xiaotong Wu, Wei-Sheng Lai, YiChang Shih +4
DSLR cameras can achieve multiple zoom levels via shifting lens distances or swapping lens types. However, these techniques are not possible on smartphone devices due to space cons…
Boundary Attention: Learning curves, corners, junctions and grouping
Mia Gaia Polansky, Charles Herrmann, Junhwa Hur +3
We present a lightweight network that infers grouping and boundaries, including curves, corners and junctions. It operates in a bottom-up fashion, analogous to classical methods fo…