6 papers · 1 filter
A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry
U-Chae Jun, Yoonhyuk Choi, Jiwoo Kang
3D scene graph prediction is commonly supervised with local object and predicate classification losses. Although effective for slot-wise label prediction, this formulation does not…
SWORD: Scene-Grounded Wasserstein Object-Relation Distance for 3D Scene Graph Prediction
U-Chae Jun, Jaeeun Ko, Yoonhyuk Choi +1
3D scene graph prediction is commonly optimized with object-node and relation-edge classification losses. These objectives encourage exact object and relation label recovery, but t…
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…