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S. Hwang

4 papers hereh-index 6119 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author1
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • S. Hwang — 29 papers, h 7
  • S. Hwang — 15 papers, h 3
  • S. Hwang — 11 papers, h 19
  • S. Hwang — 7 papers, h 13
  • S. Hwang — 6 papers, h 21
  • S. Hwang — 6 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedThe Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation

4 citations · 4 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2023

FaceCLIPNeRF: Text-driven 3D Face Manipulation using Deformable Neural Radiance Fields

Sungwon Hwang, Junha Hyung, Daejin Kim +2

As recent advances in Neural Radiance Fields (NeRF) have enabled high-fidelity 3D face reconstruction and novel view synthesis, its manipulation also became an essential task in 3D…

cs.CV2023

Local 3D Editing via 3D Distillation of CLIP Knowledge

Junha Hyung, Sungwon Hwang, Daejin Kim +2

3D content manipulation is an important computer vision task with many real-world applications (e.g., product design, cartoon generation, and 3D Avatar editing). Recently proposed…

cs.CV2023

Context-Preserving Two-Stage Video Domain Translation for Portrait Stylization

Doyeon Kim, Eunji Ko, Hyunsu Kim +5

Portrait stylization, which translates a real human face image into an artistically stylized image, has attracted considerable interest and many prior works have shown impressive q…

cs.CV2023★ 4 cited

The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation

Beomyoung Kim, Joonhyun Jeong, Dongyoon Han +1

In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.