mlsanityData to 14 Oct 2026

Countering Racial Bias in Computer Graphics Research

arXiv 2103.15163 ↗First posted 28 Mar 2021cs.GRcited by 8

Not on the map. Why?

Abstract (arXiv)

Current computer graphics research practices contain racial biases that have resulted in investigations into "skin" and "hair" that focus on the hegemonic visual features of Europeans and East Asians. To broaden our research horizons to encompass all of humanity, we propose a variety of improvements to quantitative measures and qualitative practices, and pose novel, open research problems.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Mar 2021.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 3; 2026: 1.

8 papers.

  1. 2022Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguationcited by 32
  2. 2025SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Facescited by 24
  3. 2022Towards Better User Studies in Computer Graphics and Visioncited by 11
  4. 2024Hairmony: Fairness-aware hairstyle classificationcited by 5
  5. 2025Parametric Shadow Control for Portrait Generation in Text-to-Image Diffusion Modelscited by 5
  6. 2023A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfoundercited by 1
  7. 2025POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modelingcited by 1
  8. 2026True to Tone? Quantifying Skin Tone Fidelity and Bias in Photographic-to-Virtual Human Pipelinescited by 0