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Jinlong Yang

14 papers hereh-index 162.7k citations27 works total

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

author position
  • middle author12
  • last author1

Across the 13 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CV13
  • cs.GR1
same name
  • Jinlong Yang — 97 papers, h 109
  • Jinlong Yang — 12 papers, h 10
  • Jinlong Yang — 11 papers, h 6
  • Jinlong Yang — 8 papers, h 4
  • Jinlong Yang — 6 papers
  • Jinlong Yang — 6 papers, h 3

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
20202025
most citedLearning Disentangled Avatars with Hybrid 3D Representations

13 citations · 53 across the 11 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022★ 8 cited

ECON: Explicit Clothed humans Optimized via Normal integration

Yuliang Xiu, Jinlong Yang, Xu Cao +2

The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is enabling the creation of detailed, clothed, 3D humans from images. However, existing methods…

cs.CV2022

Capturing and Animation of Body and Clothing from Monocular Video

Yao Feng, Jinlong Yang, Marc Pollefeys +2

While recent work has shown progress on extracting clothed 3D human avatars from a single image, video, or a set of 3D scans, several limitations remain. Most methods use a holisti…

cs.CV2022

Neural Point-based Shape Modeling of Humans in Challenging Clothing

Qianli Ma, Jinlong Yang, Michael J. Black +1

Parametric 3D body models like SMPL only represent minimally-clothed people and are hard to extend to clothing because they have a fixed mesh topology and resolution. To address th…

cs.CV2022★ 4 cited

gDNA: Towards Generative Detailed Neural Avatars

Xu Chen, Tianjian Jiang, Jie Song +4

To make 3D human avatars widely available, we must be able to generate a variety of 3D virtual humans with varied identities and shapes in arbitrary poses. This task is challenging…

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