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A list of works on evaluation of visual generation models, including evaluation metrics, models, and systems
| Date | Stars |
|---|---|
| 2026-07-24 | 454 |
| 2026-07-25 | 454 |
| 2026-07-28 | 454 |
| 2026-07-30 | 454 |
| 2026-08-06 | 454 |
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# Awesome Evaluation of Visual Generation *This repository collects methods for evaluating visual generation.*  ## Overview ### What You'll Find Here Within this repository, we collect works that aim to answer some critical questions in the field of evaluating visual generation, such as: - **Model Evaluation**: How does one determine the quality of a specific image or video generation model? - **Sample/Content Evaluation**: What methods can be used to evaluate the quality of a particular generated image or video? - **User Control Consistency Evaluation**: How to tell how well the generated images and videos align with the user controls or inputs? ### Updates This repository is updated periodically. If you have suggestions for additional resources, updates on methodologies, or fixes for expiring links, please feel free to do any of the following: - raise an [Issue](https://github.com/ziqihuangg/Awesome-Evaluation-of-Visual-Generation/issues), - nominate awesome related works with [Pull Requests](https://github.com/ziqihuangg/Awesome-Evaluation-of-Visual-Generation/pulls), - We are also contactable via email (`ZIQI002 at e dot ntu dot edu dot sg`). ### Table of Contents - [1. Evaluation Metrics of Generative Models](#1.) - [1.1. Evaluation Metrics of Image Generation](#1.1.) - [1.2. Evaluation Metrics of Video Generation](#1.2.) - [1.3. Evaluation Metrics for Latent Representation](#1.3.) - [2. Evaluation Metrics of Condition Consistency](#2.) - [2.1 Evaluation Metrics of Multi-Modal Condition Consistency](#2.1.) - [2.2. Evaluation Metrics of Image Similarity](#2.2.) - [3. Evaluation Systems of Generative Models](#3.) - [3.1. Evaluation of Unconditional Image Generation](#3.1.) - [3.2. Evaluation of Text-to-Image Generation](#3.2.) - [3.3. Evaluation of Text-Based Image Editing](#3.3.) - [3.4. Evaluation of Neural Style Transfer](#3.4.) - [3.5. Evaluation of Video Generation](#3.5.) - [3.6. Evaluation of Text-to-Motion Generation](#3.6.) - [3.7. Evaluation of Model Trustworthiness](#3.7.) - [3.8. Evaluation of Entity Relation](#3.8.) - [3.9. Agentic Evaluation](#3.9.) - [4. Improving Visual Generation with Evaluation / Feedback / Reward](#4.) - [5. Quality Assessment for AIGC](#5.) - [6. Study and Rethinking](#6.) - [7. Other Useful Resources](#7.) <a name="1."></a> ## 1. Evaluation Metrics of Generative Models <a name="1.1."></a> ### 1.1. Evaluation Metrics of Image Generation | Metric | Paper | Code | | -------- | -------- | ------- | | Inception Score (IS) | [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) (NeurIPS 2016) | | | Fréchet Inception Distance (FID) | [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) (NeurIPS 2017) | [](https://github.com/bioinf-jku/TTUR) [](https://github.com/mseitzer/pytorch-fid) | | Kernel Inception Distance (KID) | [Demystifying MMD GANs](https://arxiv.org/abs/1801.01401) (ICLR 2018) | [](https://github.com/toshas/torch-fidelity) [](https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/metrics/kernel_inception_distance.py) | CLIP-FID | [The Role of ImageNet Classes in Fréchet Inception Distance](https://arxiv.org/abs/2203.06026) (ICLR 2023) | [](https://github.com/kynkaat/role-of-imagenet-classes-in-fid) [![Code](https://img.shields.io/github/stars/GaParmar/clean-fid.svg?style=social&label=Official
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c6e954e6cb11b8b6, topic:benchmark, topic:evaluation
matched fp:c6e954e6cb11b8b6, topic:image-generation, readme:text-to-image, readme:image generation
matched fp:c6e954e6cb11b8b6, topic:video-generation, readme:video generation, readme:motion generation
matched fp:c6e954e6cb11b8b6, topic:awesome, desc:a list of