Xemo-Talker: Unlock Emotions Explicitly for Audio-Driven Talking Portrait Synthesis

[2608.14700] Xemo-Talker: Unlock Emotions Explicitly for Audio-Driven Talking Portrait Synthesis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14700 (cs) [Submitted on 10 Aug 2026] Title: Xemo-Talker: Unlock Emotions Explicitly for Audio-Driven Talking Portrait Synthesis Authors: Chaolong Yang , Yinuo Guo , Kai Yao , Yuyao Yan , Jie Sun , Guangliang Cheng , Shibin Wu , Bin Dong , Kaizhu Huang View a PDF of the paper titled Xemo-Talker: Unlock Emotions Explicitly for Audio-Driven Talking Portrait Synthesis, by Chaolong Yang and 8 other authors View PDF HTML (experimental) Abstract: Precise emotion control in audio-driven talking heads remains a challenge due to the reliance on implicit emotion regulation in existing systems, which often leads to indirect and insufficient control. Additionally, training with explicit emotion-related losses across the entire motion space poses significant difficulties due to the inherent trade-off between accurate lip synchronization and fine-grained emotion control. In this paper, we reveal a key finding: although emotional cues are distributed throughout the motion space, concentrating discriminative supervision on less-principal components achieves a better emotion-lip synchronization balance, as principal components mainly encode high-energy articulation and pose variations. Building on this insight, we propose Xemo-Talker, which first learns a neutral speech-to-motion mapping for stable articulation and lip synchronization, and then introduces a lightweight emotion branch guided by less-principal subspace supervision. To enhance emotion control, we design a Tri-Loss consisting of inter-class separation, intra-class compactness, and less-principal contrastive learning. Given an audio input, a reference image, and an emotion label, Xemo-Talker achieves state-of-the-art emotion classification accuracy while maintaining competitive lip synchronization and high inference efficiency, with performance approaching that measured on real this http URL source code is publicly available at this https URL . Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Sound (cs.SD) Cite as: arXiv:2608.14700 [cs.CV] (or arXiv:2608.14700v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14700 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chaolong Yang [ view email ] [v1] Mon, 10 Aug 2026 11:17:42 UTC (5,108 KB) Full-text links: Access Paper: View a PDF of the paper titled Xemo-Talker: Unlock Emotions Explicitly for Audio-Driven Talking Portrait Synthesis, by Chaolong Yang and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.SD References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition#1106098
• Oracle Complementarity Is Not Realizable Complementarity in Frozen-Encoder Audio-Visual Emotion Recognition#1115720
• MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant#1070577
• InstructFX2FX: A Multi-Turn Text-to-Effect System for Sequential Audio Effect Refinement#1123136
• Selective Lookahead for Attention-Based Streaming ASR#1141926