mlsanityData to 14 Oct 2026

Revealing Directions for Text-guided 3D Face Editing

arXiv 2410.04965 ↗First posted 7 Oct 2024cs.CVcited by 2

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Abstract (arXiv)

3D face editing is a significant task in multimedia, aimed at the manipulation of 3D face models across various control signals. The success of 3D-aware GAN provides expressive 3D models learned from 2D single-view images only, encouraging researchers to discover semantic editing directions in its latent space. However, previous methods face challenges in balancing quality, efficiency, and generalization. To solve the problem, we explore the possibility of introducing the strength of diffusion model into 3D-aware GANs. In this paper, we present Face Clan, a fast and text-general approach for generating and manipulating 3D faces based on arbitrary attribute descriptions. To achieve disentangled editing, we propose to diffuse on the latent space under a pair of opposite prompts to estimate the mask indicating the region of interest on latent codes. Based on the mask, we then apply denoising to the masked latent codes to reveal the editing direction. Our method offers a precisely controllable manipulation method, allowing users to intuitively customize regions of interest with the text description. Experiments demonstrate the effectiveness and generalization of our Face Clan for various pre-trained GANs. It offers an intuitive and wide application for text-guided face editing that contributes to the landscape of multimedia content creation.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 71 of its 84 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

71 of 84 references.

References · 71

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38 more
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50 most cited of 71 All 71 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026ESGaussianFace: Emotional and Stylized Audio-Driven Facial Animation via 3D Gaussian Splattingcited by 2
  2. 2026StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generationcited by 1

Similar, not cited

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  1. 2026A Large-scale Evaluation of Text-guided Models for Facial Editing
  2. 2024Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt Learning
  3. 2023FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
  4. 2024Controllable Face Synthesis with Semantic Latent Diffusion Models
  5. 2023ChatFace: Chat-Guided Real Face Editing via Diffusion Latent Space Manipulation
  6. 2026AttDiff-GAN: A Hybrid Diffusion-GAN Framework for Facial Attribute Editing
  7. 2023Text-Guided 3D Face Synthesis -- From Generation to Editing
  8. 2025Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation