most citedLearning Neural Light Fields with Ray-Space Embedding Networks

11 citations · 11 across the 2 of their papers we have counts for

collaborators

17 papers

cs.CV2024

Modeling Ambient Scene Dynamics for Free-view Synthesis

Meng-Li Shih, Jia-Bin Huang, Changil Kim +3

We introduce a novel method for dynamic free-view synthesis of an ambient scenes from a monocular capture bringing a immersive quality to the viewing experience. Our method builds…

cs.CV2024

Coherent Zero-Shot Visual Instruction Generation

Quynh Phung, Songwei Ge, Jia-Bin Huang

Despite the advances in text-to-image synthesis, particularly with diffusion models, generating visual instructions that require consistent representation and smooth state transiti…

cs.CV20243 cited

Recent Trends in 3D Reconstruction of General Non-Rigid Scenes

Raza Yunus, Jan Eric Lenssen, Michael Niemeyer +7

Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthe…

cs.CV2024

CTGAN: Semantic-guided Conditional Texture Generator for 3D Shapes

Yi-Ting Pan, Chai-Rong Lee, Shu-Ho Fan +4

The entertainment industry relies on 3D visual content to create immersive experiences, but traditional methods for creating textured 3D models can be time-consuming and subjective…

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.CV20232 cited

FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis

Feng Liang, Bichen Wu, Jialiang Wang +8

Diffusion models have transformed the image-to-image (I2I) synthesis and are now permeating into videos. However, the advancement of video-to-video (V2V) synthesis has been hampere…