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ALADDIN: Adversarial Latent Diffusion Distillation for One-Step Identity Swapping

Paper recorded by Signals 4 on 2026-10-27 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-27 on arXiv · recorded by Signals 4 on 2026-10-28

Category: cs.CV · 计算机视觉 · first seen 2026-10-28

Abstract

Face swapping transfers a source identity onto a target image while preserving the target's pose, expression, hair, and lighting. The most realistic current methods are diffusion-based, but they need many network evaluations per image. GAN-based methods are fast, yet trained from scratch they must learn realism from the adversarial signal alone and produce visibly weaker results. We present ALADDI

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#119 most recent of 739 cs.CV papers we have recorded · ↑ newer: A note on data-efficient finetuning of vision foundation models · ↓ older: How Reliable Is Individualized Functional-Network Attribution in Resti
Cite this page: ALADDIN: Adversarial Latent Diffusion Distillation for One-Step Identity Swapping: the #119 most recent of 739 cs.CV papers we have recorded (as of 2026-10-27). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/aladdin-adversarial-latent-diffusion-distillation-for-one-step-identity-swapping.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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