# FACEMUG: A Multimodal Generative and Fusion Framework for Local Facial Editing

arXiv: https://arxiv.org/abs/2412.19009 · HTML: https://mlsanity.com/paper/2412.19009/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 75 of its 93 references that are in the map.
- First posted on arXiv 26 Dec 2024.

## Builds on (most cited first, 30 of 75)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021)
- [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Designing an Encoder for StyleGAN Image Manipulation](https://mlsanity.com/paper/2102.02766/) (2021)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [In-Domain GAN Inversion for Real Image Editing](https://mlsanity.com/paper/2004.00049/) (2020)

## Built on by (most cited first, 6 of 6)

- [Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration](https://mlsanity.com/paper/2412.21042/) (2024)
- [MoSa: Motion Generation with Scalable Autoregressive Modeling](https://mlsanity.com/paper/2511.01200/) (2025)
- [TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models](https://mlsanity.com/paper/2503.04021/) (2025)
- [EchoSR: Efficient Context Harnessing for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2605.17470/) (2026)
- [LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation](https://mlsanity.com/paper/2604.21279/) (2026)
- [Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing](https://mlsanity.com/paper/2607.06136/) (2026)

## Abstract (arXiv)

Existing facial editing methods have achieved remarkable results, yet they often fall short in supporting multimodal conditional local facial editing. One of the significant evidences is that their output image quality degrades dramatically after several iterations of incremental editing, as they do not support local editing. In this paper, we present a novel multimodal generative and fusion framework for globally-consistent local facial editing (FACEMUG) that can handle a wide range of input modalities and enable fine-grained and semantic manipulation while remaining unedited parts unchanged. Different modalities, including sketches, semantic maps, color maps, exemplar images, text, and attribute labels, are adept at conveying diverse conditioning details, and their combined synergy can provide more explicit guidance for the editing process. We thus integrate all modalities into a unified generative latent space to enable multimodal local facial edits. Specifically, a novel multimodal feature fusion mechanism is proposed by utilizing multimodal aggregation and style fusion blocks to fuse facial priors and multimodalities in both latent and feature spaces. We further introduce a novel self-supervised latent warping algorithm to rectify misaligned facial features, efficiently transferring the pose of the edited image to the given latent codes. We evaluate our FACEMUG through extensive experiments and comparisons to state-of-the-art (SOTA) methods. The results demonstrate the superiority of FACEMUG in terms of editing quality, flexibility, and semantic control, making it a promising solution for a wide range of local facial editing tasks.