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Junying Wang

4 papers hereh-index 335 citations7 works total

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

author position
  • first author2
  • middle author2

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

fields
  • cs.CV4
same name
  • Junying Wang — 13 papers, h 4
  • Junying Wang — 2 papers, h 3
  • Junying Wang — 1 paper, h 25
  • Junying Wang — 1 paper, h 2

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

activity
20232025
most citedMeta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2025

DGH: Dynamic Gaussian Hair

Junying Wang, Yuanlu Xu, Edith Tretschk +5

The creation of photorealistic dynamic hair remains a major challenge in digital human modeling because of the complex motions, occlusions, and light scattering. Existing methods o…

cs.CV2025

Comprehensive Relighting: Generalizable and Consistent Monocular Human Relighting and Harmonization

Junying Wang, Jingyuan Liu, Xin Sun +9

This paper introduces Comprehensive Relighting, the first all-in-one approach that can both control and harmonize the lighting from an image or video of humans with arbitrary body…

cs.CV2024

Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization

Cho-Ying Wu, Yiqi Zhong, Junying Wang +1

Indoor robots rely on depth to perform tasks like navigation or obstacle detection, and single-image depth estimation is widely used to assist perception. Most indoor single-image…

cs.CV2023★ 1 cited

Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction

Cho-Ying Wu, Yiqi Zhong, Junying Wang +1

Model generalizability to unseen datasets, concerned with in-the-wild robustness, is less studied for indoor single-image depth prediction. We leverage gradient-based meta-learning…

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