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Updated: Mar 6, 2026

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

GSwap: Realistic Head Swapping With Dynamic Neural Gaussian Field.

Jingtao Zhou, Xuan Gao, Dongyu Liu

IEEE Transactions on Visualization and Computer Graphics

|March 4, 2026

Summary

This summary is machine-generated.

GSwap introduces a novel video head-swapping system using dynamic neural Gaussian priors for realistic and consistent face replacement. This method overcomes limitations of 2D models and 3D Morphable Face Models (3DMM), improving 3D consistency and realism.

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Area of Science:

Computer Vision
Computer Graphics
Artificial Intelligence

Background:

Existing video head-swapping methods often struggle with 3D consistency, natural expression synthesis, and seamless background integration.
Previous approaches using 2D generative models or 3D Morphable Face Models (3DMM) have inherent limitations in holistic head modeling and realism.

Purpose of the Study:

To develop a novel and realistic video head-swapping system that addresses the limitations of current state-of-the-art methods.
To achieve high-fidelity, 3D-consistent portrait rendering with natural head-torso relationships and seamless motion dynamics.

Main Methods:

Introduced GSwap, a system leveraging dynamic neural Gaussian portrait priors and a 3D Gaussian feature field within a full-body SMPL-X surface.
Adapted a pretrained 2D portrait generative model for efficient domain adaptation using few-shot learning.
Proposed a neural re-rendering strategy for seamless foreground-background integration, eliminating blending artifacts.

Main Results:

GSwap significantly advances the state of the art in face and head replacement, demonstrating superior performance over existing methods.
Achieved high-fidelity, 3D-consistent portrait rendering with natural facial expressions and seamless motion.
Successfully integrated synthesized foregrounds with original backgrounds, enhancing overall realism and eliminating artifacts.

Conclusions:

GSwap offers a robust solution for video head-swapping, outperforming previous techniques in visual quality, temporal coherence, identity preservation, and 3D consistency.
The dynamic neural Gaussian field approach combined with neural re-rendering provides a significant leap forward in realistic and consistent digital human synthesis.