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  1. Home
  3. Swapcontroller: Towards Improving Identity And Attribute Control For Diffusion-based Face Swapping.
  1. Home
  3. Swapcontroller: Towards Improving Identity And Attribute Control For Diffusion-based Face Swapping.

Related Experiment Video

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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Published on: March 1, 2017

SwapController: Towards Improving Identity and Attribute Control for Diffusion-Based Face Swapping.

Fengyuan Liu, Lingyun Yu, Quanwei Yang

    IEEE Transactions on Visualization and Computer Graphics
    |June 10, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    SwapController, a novel diffusion-based face-swapping framework, enhances identity and attribute consistency in generated faces. It achieves high-fidelity results by leveraging 3D facial priors and novel embedding techniques for accurate facial representation.

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    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

    Published on: June 3, 2013

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Deep Learning

    Background:

    • Diffusion models offer high-fidelity face generation but struggle with identity and attribute consistency.
    • Accurate modeling of facial representations remains a challenge in current face-swapping technologies.

    Purpose of the Study:

    • To introduce SwapController, a novel diffusion-based framework for high-fidelity face swapping.
    • To improve identity and attribute consistency in generated faces using advanced modeling techniques.

    Main Methods:

    • Utilizing 3D facial priors for explicit structure supervision and accurate head structure control.
    • Developing multi-grained source identity embeddings for enhanced identity preservation.
    • Creating identity-shifted attribute embeddings for improved attribute modeling.
  • Implementing a timestep-aware identity optimization objective for diffusion models.

    • Main Results:

    • SwapController demonstrates effectiveness in generating identity-consistent portraits while preserving target attributes.
    • Achieved a 98.32 ID Retrieval score, surpassing the state-of-the-art by 7.32.
    • The framework successfully balances high-fidelity generation with precise control over facial identity and attributes.

    Conclusions:

    • SwapController offers a robust solution for high-fidelity face swapping with superior identity and attribute control.
    • The proposed methods effectively address the limitations of diffusion models in maintaining facial consistency.
    • This framework advances the field of generative AI for realistic and controllable face manipulation.