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Shanshan Zhao

4 papers hereh-index 5122 citations11 works total

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

author position
  • middle author4

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

fields
  • cs.CV4
same name
  • Shanshan Zhao — 8 papers, h 6
  • Shanshan Zhao — 3 papers, h 2
  • Shanshan Zhao — 1 paper, h 7
  • Shanshan Zhao — 1 paper, h 2
  • Shanshan Zhao — 1 paper
  • Shanshan Zhao — 1 paper, h 1

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
20242026
collaborators

4 papers

cs.CV2026

InpaintHuman: Reconstructing Occluded Humans with Multi-Scale UV Mapping and Identity-Preserving Diffusion Inpainting

Jinlong Fan, Shanshan Zhao, Liang Zheng +3

Reconstructing complete and animatable 3D human avatars from monocular videos remains challenging, particularly under severe occlusions. While 3D Gaussian Splatting has enabled pho…

cs.CV2025

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning

Yiyang Chen, Shanshan Zhao, Lunhao Duan +2

Diffusion-based models, widely used in text-to-image generation, have proven effective in 2D representation learning. Recently, this framework has been extended to 3D self-supervis…

cs.CV2024

When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on its Contour-following Ability

Wenjie Xuan, Yufei Xu, Shanshan Zhao +4

ControlNet excels at creating content that closely matches precise contours in user-provided masks. However, when these masks contain noise, as a frequent occurrence with non-exper…

cs.CV2024

Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment

Liyao Tang, Zhe Chen, Shanshan Zhao +2

Label-efficient segmentation aims to perform effective segmentation on input data using only sparse and limited ground-truth labels for training. This topic is widely studied in 3D…

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