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New framework EmoteGPT generates 3D facial expressions from text

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced EmoteGPT, a novel framework for generating 3D human facial expressions from natural language descriptions. This system utilizes a multimodal large language model with a specialized token to translate textual inputs into parameters for a 3D Morphable Model. The framework was trained on a new benchmark dataset called Txt2Emote, which includes detailed textual annotations for expressions, and further enhanced with image-to-3DMM data. EmoteGPT demonstrates superior performance in expressiveness and emotion recognition compared to existing text-to-3D face synthesis methods. AI

IMPACT Enables more realistic and controllable 3D avatars and virtual interactions through advanced text-to-expression generation.

RANK_REASON The cluster contains a research paper describing a new model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework EmoteGPT generates 3D facial expressions from text

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The cluster contains a research paper describing a new model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Haoran Wang, Mohit Mendiratta, Christian Theobalt, Adam Kortylewski ·

    EmoteGPT: 3D Human Facial Expressions from Natural Language Descriptions

    arXiv:2607.02674v1 Announce Type: new 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 ex…

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