6 papers
NullFlow: One-Step Generative Reconstruction
Xiao Shi, Edward P. Chandler, Chicago Y. Park +2
We propose NullFlow, a principled framework for one-step generative image reconstruction. Our key idea is to confine the generative flow to a measurement-consistent subspace. Becau…
FlexDrive: Toward Trajectory Flexibility in Driving Scene Reconstruction and Rendering
Jingqiu Zhou, Lue Fan, Linjiang Huang +4
Driving scene reconstruction and rendering have advanced significantly using the 3D Gaussian Splatting. However, most prior research has focused on the rendering quality along a pr…
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events
Yijin Li, Yichen Shen, Zhaoyang Huang +9
Recent advances in event-based vision suggest that these systems complement traditional cameras by providing continuous observation without frame rate limitations and a high dynami…
GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking
Weikang Bian, Zhaoyang Huang, Xiaoyu Shi +3
4D video control is essential in video generation as it enables the use of sophisticated lens techniques, such as multi-camera shooting and dolly zoom, which are currently unsuppor…
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation
Yijin Li, Zhaoyang Huang, Shuo Chen +5
Event cameras provide high temporal precision, low data rates, and high dynamic range visual perception, which are well-suited for optical flow estimation. While data-driven optica…
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
Fu-Yun Wang, Zhaoyang Huang, Weikang Bian +5
This paper introduces an effective method for computation-efficient personalized style video generation without requiring access to any personalized video data. It reduces the nece…