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Tao Huang

6 papers hereh-index 29 citations5 works total

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

author position
  • first author1
  • middle author4
  • last author1

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.GR4
  • cs.CV1
  • cs.LG1
same name
  • Tao Huang — 270 papers
  • Tao Huang — 87 papers, h 24
  • Tao Huang — 22 papers, h 49
  • Tao Huang — 22 papers, h 7
  • Tao Huang — 21 papers, h 12
  • Tao Huang — 21 papers, h 17

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 citedEfficient Scene Appearance Aggregation for Level-of-Detail Rendering

6 citations · 6 across the 5 of their papers we have counts for

collaborators
Showing cs.GRShow all

4 papers · 1 filter

cs.GR2026

Real-Time Neural Hair G-Buffer Anti-Aliasing

Chenghao Wu, Yuefan Shen, Tao Huang +3

We propose a lightweight real-time method for reconstructing strand-based hair G-Buffers from severely undersampled rasterized inputs. Our pipeline first applies neural spatial rec…

cs.GR2026

Real-time Neural Six-way Lightmaps

Wei Li, Hanxiao Sun, Tao Huang +4

Participating media are a pervasive and intriguing visual effect in virtual environments. Unfortunately, rendering such phenomena in real-time is notoriously difficult due to the c…

cs.GR2024★ 6 cited

Efficient Scene Appearance Aggregation for Level-of-Detail Rendering

Yang Zhou, Tao Huang, Ravi Ramamoorthi +2

Creating an appearance-preserving level-of-detail (LoD) representation for arbitrary 3D scenes is a challenging problem. The appearance of a scene is an intricate combination of bo…

cs.GR2024

Real-time Level-of-Detail Strand-based Hair Rendering

Tao Huang, Yang Zhou, Daqi Lin +3

Strand-based hair rendering has become increasingly popular in production for its realistic appearance. However, the prevailing level-of-detail solution employing hair cards for di…

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