8 papers
Image Generators are Generalist Vision Learners
Valentin Gabeur, Shangbang Long, Songyou Peng +22
Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language under…
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…
Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving
Jiahao Wang, Bo Sun, Yijing Bai +12
Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidel…
GeoFlow: Enforcing Implicit Geometric Consistency in Video Generation
Jan Ackermann, Shengqu Cai, Boyang Deng +3
Generating geometrically consistent videos remains an open challenge: text-to-video diffusion models trained on web-scale data treat geometry only implicitly, leading to object def…
Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
Weijie Wang, Qihang Cao, Sensen Gao +10
Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understanding and interacting with the physical…
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…