EmoteGPT: 3D Human Facial Expressions from Natural Language Descriptions

• URL: http://arxiv.org/abs/2607.02674v1
• Date: Thu, 02 Jul 2026 18:10:34 GMT
• Title: EmoteGPT: 3D Human Facial Expressions from Natural Language Descriptions
• Abstract summary: We introduce Txt2Emote, a benchmark of diverse 3D facial expressions with fine-grained textual annotations.<n>We present EmoteGPT, a text-to-3D expression framework based on a Multimodal Large Language Model.<n>We further improve EmoteGPT by augmenting training with large-scale image-to-3DMM data, enabling it to surpass state-of-the-art text-to-3D face synthesis methods.
• Score: 67.8321457457786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise control of 3D facial expressions from text is crucial for virtual avatars, animation, and human-computer interaction, yet existing text-to-3D methods jointly generate identity, expression, and texture, making fine-grained expression control difficult. We instead formulate text-driven expression synthesis as a regression problem in the disentangled parameter space of a 3D Morphable Model (3DMM). This setting, however, requires paired data linking detailed language to precise expression parameters, which are missing from existing resources. To fill this gap, we introduce Txt2Emote, a benchmark of diverse 3D facial expressions with fine-grained textual annotations obtained from GPT-4o and a high-fidelity face tracker, providing both explicit descriptions detailing facial features and implicit descriptions referencing the situational context behind the expression. Leveraging this dataset, we present EmoteGPT, a text-to-3D expression framework based on a Multimodal Large Language Model (MLLM) with a dedicated <Expr> token to semantically ground expression representations, which are then decoded into 3DMM parameters. We further improve EmoteGPT by augmenting training with large-scale image-to-3DMM data, enabling it to surpass state-of-the-art text-to-3D face synthesis methods on emotion recognition metrics and in perceived expressiveness. Integrated into avatar pipelines, our method enables photorealistic and stylized 3D avatars, as well as expressive 3D-consistent 2D face synthesis from textual input.

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