5 papers
View-Adaptive Renderer for View-Consistent 2D-to-3D Generation
U-Chae Jun, Jaeeun Ko, Jiwoo Kang
Reconstructing 3D shapes from a single image remains a fundamental yet challenging problem in computer vision. Traditional monocular 3D generation pipelines typically synthesize mu…
Convolutional Neural Shading for High-Quality 3D Reconstruction from Multi-View Images
Juheon Hwang, Taewan Kim, Heeseok Oh +1
We propose a convolutional neural shading (CNS), a novel pipeline to reconstruct high-quality 3D shapes from multi-view images. Several recent studies have used neural radiance fie…
Collaborative Feature Aggregation for Face Super-Resolution and Robust Re-Identification
Juheon Hwang, Taewan Kim, Jiwoo Kang
We propose a novel collaborative approach for face super-resolution (SR) and robust person re-identification from sequential or multi-view facial images. Traditional SR methods oft…
Face and Voice Cross-modal Association with Learning Convex Feature Embedding
Taewan Kim, Jiwoo Kang
Face-and-voice association learning is one of the most challenging tasks in deep learning. In this paper, we propose a simple but powerful cross-modal feature embedding method for…
GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar
SeungJun Moon, Hah Min Lew, Seungeun Lee +2
Despite recent progress in 3D head avatar generation, balancing identity preservation, i.e., reconstruction, with novel poses and expressions, i.e., animation, remains a challenge.…