most citedLearning Neural Light Fields with Ray-Space Embedding Networks

11 citations · 11 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV20241 cited

TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion

Yu-Ying Yeh, Jia-Bin Huang, Changil Kim +8

We present TextureDreamer, a novel image-guided texture synthesis method to transfer relightable textures from a small number of input images (3 to 5) to target 3D shapes across ar…

cs.CV2023

OmnimatteRF: Robust Omnimatte with 3D Background Modeling

Geng Lin, Chen Gao, Jia-Bin Huang +4

Video matting has broad applications, from adding interesting effects to casually captured movies to assisting video production professionals. Matting with associated effects such…

cs.CV2023

Consistent View Synthesis with Pose-Guided Diffusion Models

Hung-Yu Tseng, Qinbo Li, Changil Kim +3

Novel view synthesis from a single image has been a cornerstone problem for many Virtual Reality applications that provide immersive experiences. However, most existing techniques…

cs.CV20233 cited

Progressively Optimized Local Radiance Fields for Robust View Synthesis

Andreas Meuleman, Yu-Lun Liu, Chen Gao +4

We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing…

cs.CV202111 cited

Learning Neural Light Fields with Ray-Space Embedding Networks

Benjamin Attal, Jia-Bin Huang, Michael Zollhoefer +2

Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results. However, they are slow to render, requiring hundreds of network evaluations per pixel to approximate…