DiffSwap++: 3D Latent-Controlled Diffusion for Identity-Preserving Face Swapping

• URL: http://arxiv.org/abs/2511.05575v1
• Date: Tue, 04 Nov 2025 18:56:49 GMT
• Title: DiffSwap++: 3D Latent-Controlled Diffusion for Identity-Preserving Face Swapping
• Authors: Weston Bondurant, Arkaprava Sinha, Hieu Le, Srijan Das, Stephanie Schuckers,
• Abstract summary: We propose DiffSwap++, a novel diffusion-based face-swapping pipeline that incorporates 3D facial latent features during training.<n>Our method enhances geometric consistency and improves the disentanglement of facial identity from appearance attributes.<n>Experiments on CelebA, FFHQ, and CelebV-Text demonstrate that DiffSwap++ outperforms prior methods in preserving source identity while maintaining target pose and expression.
• Score: 16.846179110602737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based approaches have recently achieved strong results in face swapping, offering improved visual quality over traditional GAN-based methods. However, even state-of-the-art models often suffer from fine-grained artifacts and poor identity preservation, particularly under challenging poses and expressions. A key limitation of existing approaches is their failure to meaningfully leverage 3D facial structure, which is crucial for disentangling identity from pose and expression. In this work, we propose DiffSwap++, a novel diffusion-based face-swapping pipeline that incorporates 3D facial latent features during training. By guiding the generation process with 3D-aware representations, our method enhances geometric consistency and improves the disentanglement of facial identity from appearance attributes. We further design a diffusion architecture that conditions the denoising process on both identity embeddings and facial landmarks, enabling high-fidelity and identity-preserving face swaps. Extensive experiments on CelebA, FFHQ, and CelebV-Text demonstrate that DiffSwap++ outperforms prior methods in preserving source identity while maintaining target pose and expression. Additionally, we introduce a biometric-style evaluation and conduct a user study to further validate the realism and effectiveness of our approach. Code will be made publicly available at https://github.com/WestonBond/DiffSwapPP

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