Institution profile

Sookmyung Women's University

Academic institutionasia · kr
Official website
Research library11linked papers
Opportunities0open roles
Selected work

Representative Papers

View-Adaptive Renderer for View-Consistent 2D-to-3D Generation

Aug 10, 2026

This work addresses the challenges of view inconsistency and geometric distortion in single-image 3D reconstruction, which often arise from projection ambiguities during multi-view synthesis. To mitigate these issues, the authors propose a view-adaptive neural rendering framework that employs a shared feature backbone to capture global structure while enabling per-view independent correction of rendering errors. A lightweight self-attention fusion module is introduced to integrate multi-view information and enhance geometric consistency without relying on supervision from diffusion models such as SDS. The method optimizes solely with photometric loss, achieving near state-of-the-art reconstruction fidelity while maintaining computational efficiency and significantly improving view consistency and practical performance.

0 citationsRead paper

Face and Voice Cross-modal Association with Learning Convex Feature Embedding

Jul 30, 2026

This work addresses the high false positive and false negative rates in cross-modal association between faces and voices, which stem from modality heterogeneity. To mitigate this issue, the authors propose a joint learning framework that integrates convex hull feature embedding with a cross-modal attention mechanism. By compactly aggregating cross-modal features of the same identity within a unified embedding space and incorporating deep metric learning, the method effectively narrows the semantic gap across modalities. Experimental results on the VoxCeleb dataset demonstrate that the proposed approach significantly outperforms state-of-the-art methods in cross-modal verification, matching, and retrieval tasks, achieving substantial reductions in error rates.

0 citationsRead paper
Recent publications

Latest Papers

View-Adaptive Renderer for View-Consistent 2D-to-3D Generation

Aug 10, 2026

This work addresses the challenges of view inconsistency and geometric distortion in single-image 3D reconstruction, which often arise from projection ambiguities during multi-view synthesis. To mitigate these issues, the authors propose a view-adaptive neural rendering framework that employs a shared feature backbone to capture global structure while enabling per-view independent correction of rendering errors. A lightweight self-attention fusion module is introduced to integrate multi-view information and enhance geometric consistency without relying on supervision from diffusion models such as SDS. The method optimizes solely with photometric loss, achieving near state-of-the-art reconstruction fidelity while maintaining computational efficiency and significantly improving view consistency and practical performance.

0 citationsRead paper

Face and Voice Cross-modal Association with Learning Convex Feature Embedding

Jul 30, 2026

This work addresses the high false positive and false negative rates in cross-modal association between faces and voices, which stem from modality heterogeneity. To mitigate this issue, the authors propose a joint learning framework that integrates convex hull feature embedding with a cross-modal attention mechanism. By compactly aggregating cross-modal features of the same identity within a unified embedding space and incorporating deep metric learning, the method effectively narrows the semantic gap across modalities. Experimental results on the VoxCeleb dataset demonstrate that the proposed approach significantly outperforms state-of-the-art methods in cross-modal verification, matching, and retrieval tasks, achieving substantial reductions in error rates.

0 citationsRead paper