17 citations · 45 across the 15 of their papers we have counts for
16 papers
LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory
Junyi Zhang, Charles Herrmann, Junhwa Hur +5
Feedforward geometric foundation models achieve strong short-window reconstruction, yet scaling them to minutes-long videos is bottlenecked by quadratic attention complexity or lim…
CityRAG: Stepping Into a City via Spatially-Grounded Video Generation
Gene Chou, Charles Herrmann, Kyle Genova +6
We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video generative models can produc…
UFO-4D: Unposed Feedforward 4D Reconstruction from Two Images
Junhwa Hur, Charles Herrmann, Songyou Peng +4
Dense 4D reconstruction from unposed images remains a critical challenge, with current methods relying on slow test-time optimization or fragmented, task-specific feedforward model…
GeCo: Evaluating Geometric Consistency for Video Generation via Motion and Structure
Leslie Gu, Junhwa Hur, Charles Herrmann +4
We introduce GeCo, a geometry-grounded metric for jointly detecting geometric deformation and occlusion-inconsistency artifacts in static scenes. By fusing residual motion and dept…
MASIV: Toward Material-Agnostic System Identification from Videos
Yizhou Zhao, Haoyu Chen, Chunjiang Liu +7
System identification from videos aims to recover object geometry and governing physical laws. Existing methods integrate differentiable rendering with simulation but rely on prede…
Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals
Nate Gillman, Charles Herrmann, Michael Freeman +4
Recent advances in video generation models have sparked interest in world models capable of simulating realistic environments. While navigation has been well-explored, physically m…
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…
WonderWorld: Interactive 3D Scene Generation from a Single Image
Hong-Xing Yu, Haoyi Duan, Charles Herrmann +2
We present WonderWorld, a novel framework for interactive 3D scene generation that enables users to interactively specify scene contents and layout and see the created scenes in lo…
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…
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model
Saurabh Saxena, Junhwa Hur, Charles Herrmann +2
While methods for monocular depth estimation have made significant strides on standard benchmarks, zero-shot metric depth estimation remains unsolved. Challenges include the joint…
WonderJourney: Going from Anywhere to Everywhere
Hong-Xing Yu, Haoyi Duan, Junhwa Hur +8
We introduce WonderJourney, a modularized framework for perpetual 3D scene generation. Unlike prior work on view generation that focuses on a single type of scenes, we start at any…
Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
Junyi Zhang, Charles Herrmann, Junhwa Hur +4
While pre-trained large-scale vision models have shown significant promise for semantic correspondence, their features often struggle to grasp the geometry and orientation of insta…
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image
Kyle Sargent, Zizhang Li, Tanmay Shah +8
We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked b…