Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation
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Abstract (arXiv)
In 3D speech-driven facial animation generation, existing methods commonly employ pre-trained self-supervised audio models as encoders. However, due to the prevalence of phonetically similar syllables with distinct lip shapes in language, these near-homophone syllables tend to exhibit significant coupling in self-supervised audio feature spaces, leading to the averaging effect in subsequent lip motion generation. To address this issue, this paper proposes a plug-and-play semantic decorrelation module-Wav2Sem. This module extracts semantic features corresponding to the entire audio sequence, leveraging the added semantic information to decorrelate audio encodings within the feature space, thereby achieving more expressive audio features. Extensive experiments across multiple Speech-driven models indicate that the Wav2Sem module effectively decouples audio features, significantly alleviating the averaging effect of phonetically similar syllables in lip shape generation, thereby enhancing the precision and naturalness of facial animations. Our source code is available at https://github.com/wslh852/Wav2Sem.git.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 33 of its 46 references that are in the map. First posted on arXiv 29 May 2025.
Builds on
33 of 46 references.
References · 33
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21 more
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Built on by
Citing papers per year: 2025: 2; 2026: 6.
8 papers.
- 2025Mitigating Error Accumulation in Co-Speech Motion Generation via Global Rotation Diffusion and Multi-Level Constraintscited by 12
- 2026RAM: Recover Any 3D Human Motion in-the-Wildcited by 12
- 2026Integrating Facial Generation into Full-Duplex Spoken Dialogue Systemscited by 2
- 2025CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysiscited by 1
- 2026A2-LLM: An End-to-end Conversational Audio Avatar Large Language Modelcited by 1
- 2026AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Modelcited by 0
- 2026ECHO: Dyadic 3D Facial Motion Generation with Asymmetric Deterministic Articulation and Stochastic Reactioncited by 0
- 2026Seeing Speech: Learning Visible Articulatory Dynamics for Speech-Driven 3D Facial Animationcited by 0
- 2026Seeing Speech: Learning Visible Articulatory Dynamics for Speech-Driven 3D Facial Animation24 Sep 2026
- 2026ECHO: Dyadic 3D Facial Motion Generation with Asymmetric Deterministic Articulation and Stochastic Reaction29 Aug 2026
- 2026AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model17 Aug 2026
- 2026Integrating Facial Generation into Full-Duplex Spoken Dialogue Systems20 Jun 2026
- 2026RAM: Recover Any 3D Human Motion in-the-Wild20 Mar 2026
- 2026A2-LLM: An End-to-end Conversational Audio Avatar Large Language Model4 Feb 2026
- 2025Mitigating Error Accumulation in Co-Speech Motion Generation via Global Rotation Diffusion and Multi-Level Constraints13 Nov 2025
- 2025CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis13 Oct 2025