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20232025
most citedVideo ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models

3 citations · 4 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting

Juncheng Chen, Chao Xu, Yanjun Cao

Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) enables photorealistic novel-view s…

cs.CV2025

4D Neural Voxel Splatting: Dynamic Scene Rendering with Voxelized Guassian Splatting

Chun-Tin Wu, Jun-Cheng Chen

Although 3D Gaussian Splatting (3D-GS) achieves efficient rendering for novel view synthesis, extending it to dynamic scenes still results in substantial memory overhead from repli…

cs.CV2024

A Recipe for CAC: Mosaic-based Generalized Loss for Improved Class-Agnostic Counting

Tsung-Han Chou, Brian Wang, Wei-Chen Chiu +1

Class agnostic counting (CAC) is a vision task that can be used to count the total occurrence number of any given reference objects in the query image. The task is usually formulat…

cs.CV2023

MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance

Ernie Chu, Tzuhsuan Huang, Shuo-Yen Lin +1

This study introduces an efficient and effective method, MeDM, that utilizes pre-trained image Diffusion Models for video-to-video translation with consistent temporal flow. The pr…

cs.CV2023★ 1 cited

Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector

Shuo-Yen Lin, Ernie Chu, Che-Hsien Lin +2

Many physical adversarial patch generation methods are widely proposed to protect personal privacy from malicious monitoring using object detectors. However, they usually fail to g…

cs.CV2023★ 3 cited

Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models

Ernie Chu, Shuo-Yen Lin, Jun-Cheng Chen

In this study, we present an efficient and effective approach for achieving temporally consistent synthetic-to-real video translation in videos of varying lengths. Our method lever…