ID-Consistent, Precise Expression Generation with Blendshape-Guided Diffusion

• URL: http://arxiv.org/abs/2510.04706v1
• Date: Mon, 06 Oct 2025 11:20:56 GMT
• Title: ID-Consistent, Precise Expression Generation with Blendshape-Guided Diffusion
• Authors: Foivos Paraperas Papantoniou, Stefanos Zafeiriou,
• Abstract summary: We present a diffusion-based framework that faithfully reimagines any subject under any particular facial expression.<n>Our adapter generalizes beyond basic emotions to subtle micro-expressions and expressive transitions, overlooked by prior works.<n>In addition, a pluggable Reference Adapter enables expression editing in real images by transferring the appearance from a reference frame during synthesis.
• Score: 40.50436862878818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-centric generative models designed for AI-driven storytelling must bring together two core capabilities: identity consistency and precise control over human performance. While recent diffusion-based approaches have made significant progress in maintaining facial identity, achieving fine-grained expression control without compromising identity remains challenging. In this work, we present a diffusion-based framework that faithfully reimagines any subject under any particular facial expression. Building on an ID-consistent face foundation model, we adopt a compositional design featuring an expression cross-attention module guided by FLAME blendshape parameters for explicit control. Trained on a diverse mixture of image and video data rich in expressive variation, our adapter generalizes beyond basic emotions to subtle micro-expressions and expressive transitions, overlooked by prior works. In addition, a pluggable Reference Adapter enables expression editing in real images by transferring the appearance from a reference frame during synthesis. Extensive quantitative and qualitative evaluations show that our model outperforms existing methods in tailored and identity-consistent expression generation. Code and models can be found at https://github.com/foivospar/Arc2Face.

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