# DDH-QA: A Dynamic Digital Humans Quality Assessment Database

arXiv: https://arxiv.org/abs/2212.12734 · HTML: https://mlsanity.com/paper/2212.12734/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 24 Dec 2022.

## Builds on (most cited first, 13 of 13)

- [FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling](https://mlsanity.com/paper/2207.02595/) (2022)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric](https://mlsanity.com/paper/2012.11895/) (2020)
- [No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models](https://mlsanity.com/paper/2107.02041/) (2021)
- [Perceptual Quality Assessment of Colored 3D Point Clouds](https://mlsanity.com/paper/2111.05474/) (2021)
- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metric](https://mlsanity.com/paper/2202.02397/) (2022)
- [A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences](https://mlsanity.com/paper/2206.05054/) (2022)
- [Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric](https://mlsanity.com/paper/2208.14085/) (2022)
- [Perceptual Quality Assessment for Digital Human Heads](https://mlsanity.com/paper/2209.09489/) (2022)

## Built on by (most cited first, 9 of 9)

- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis](https://mlsanity.com/paper/2411.11235/) (2024)
- [THQA: A Perceptual Quality Assessment Database for Talking Heads](https://mlsanity.com/paper/2404.09003/) (2024)
- [Dual-Branch Network for Portrait Image Quality Assessment](https://mlsanity.com/paper/2405.08555/) (2024)
- [Geometry-Aware Video Quality Assessment for Dynamic Digital Human](https://mlsanity.com/paper/2310.15984/) (2023)
- [A No-Reference Quality Assessment Method for Digital Human Head](https://mlsanity.com/paper/2310.16732/) (2023)
- [3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents](https://mlsanity.com/paper/2409.07236/) (2024)
- [DHQA-4D: Perceptual Quality Assessment of Dynamic 4D Digital Human](https://mlsanity.com/paper/2510.03874/) (2025)
- [Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads](https://mlsanity.com/paper/2507.23343/) (2025)

## Abstract (arXiv)

In recent years, large amounts of effort have been put into pushing forward the real-world application of dynamic digital human (DDH). However, most current quality assessment research focuses on evaluating static 3D models and usually ignores motion distortions. Therefore, in this paper, we construct a large-scale dynamic digital human quality assessment (DDH-QA) database with diverse motion content as well as multiple distortions to comprehensively study the perceptual quality of DDHs. Both model-based distortion (noise, compression) and motion-based distortion (binding error, motion unnaturalness) are taken into consideration. Ten types of common motion are employed to drive the DDHs and a total of 800 DDHs are generated in the end. Afterward, we render the video sequences of the distorted DDHs as the evaluation media and carry out a well-controlled subjective experiment. Then a benchmark experiment is conducted with the state-of-the-art video quality assessment (VQA) methods and the experimental results show that existing VQA methods are limited in assessing the perceptual loss of DDHs.