Gen-AFFECT: Generation of Avatar Fine-grained Facial Expressions with Consistent identiTy

• URL: http://arxiv.org/abs/2508.09461v1
• Date: Wed, 13 Aug 2025 03:35:35 GMT
• Title: Gen-AFFECT: Generation of Avatar Fine-grained Facial Expressions with Consistent identiTy
• Authors: Hao Yu, Rupayan Mallick, Margrit Betke, Sarah Adel Bargal,
• Abstract summary: GEN-AFFECT is a novel framework for personalized avatar generation.<n>It generates expressive and identity-consistent avatars with a diverse set of facial expressions.
• Score: 15.26953477181137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Different forms of customized 2D avatars are widely used in gaming applications, virtual communication, education, and content creation. However, existing approaches often fail to capture fine-grained facial expressions and struggle to preserve identity across different expressions. We propose GEN-AFFECT, a novel framework for personalized avatar generation that generates expressive and identity-consistent avatars with a diverse set of facial expressions. Our framework proposes conditioning a multimodal diffusion transformer on an extracted identity-expression representation. This enables identity preservation and representation of a wide range of facial expressions. GEN-AFFECT additionally employs consistent attention at inference for information sharing across the set of generated expressions, enabling the generation process to maintain identity consistency over the array of generated fine-grained expressions. GEN-AFFECT demonstrates superior performance compared to previous state-of-the-art methods on the basis of the accuracy of the generated expressions, the preservation of the identity and the consistency of the target identity across an array of fine-grained facial expressions.

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