activity
20202026
most citedAnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SP2026

GVC-RT: Towards Real-Time Generative Video Compression at Ultra-Low Bitrates

Tianjian Dang, Sixian Wang, Lei Luo +2

Recent generative video codecs (GVCs) have achieved impressive reconstruction fidelity at ultra-low bitrates (< 0.02 bits per pixel) by compressing the tokens from generative token…

cs.CV2026

Free-Range Gaussians: Non-Grid-Aligned Generative 3D Gaussian Reconstruction

Ahan Shabanov, Peter Hedman, Ethan Weber +10

We present Free-Range Gaussians, a multi-view reconstruction method that predicts non-pixel, non-voxel-aligned 3D Gaussians from as few as four images. This is done through flow ma…

cs.CV2026

LaVR: Scene Latent Conditioned Generative Video Trajectory Re-Rendering using Large 4D Reconstruction Models

Mingyang Xie, Numair Khan, Tianfu Wang +8

Given a monocular video, the goal of video re-rendering is to generate views of the scene from a novel camera trajectory. Existing methods face two distinct challenges. Geometrical…

cs.CV2023★ 2 cited

AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation

Hyunyoung Jung, Zhuo Hui, Lei Luo +5

To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs. However, downsizing inputs makes the esti…

cs.CV2020★ 1 cited

Shape-aware Generative Adversarial Networks for Attribute Transfer

Lei Luo, William Hsu, Shangxian Wang

Generative adversarial networks (GANs) have been successfully applied to transfer visual attributes in many domains, including that of human face images. This success is partly att…