{"ID":2834350,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01340","arxiv_id":"2512.01340","title":"EvalTalker: Learning to Evaluate Real-Portrait-Driven Multi-Subject Talking Humans","abstract":"Speech-driven Talking Human (TH) generation, commonly known as \"Talker,\" currently faces limitations in multi-subject driving capabilities. Extending this paradigm to \"Multi-Talker,\" capable of animating multiple subjects simultaneously, introduces richer interactivity and stronger immersion in audiovisual communication. However, current Multi-Talkers still exhibit noticeable quality degradation caused by technical limitations, resulting in suboptimal user experiences. To address this challenge, we construct THQA-MT, the first large-scale Multi-Talker-generated Talking Human Quality Assessment dataset, consisting of 5,492 Multi-Talker-generated THs (MTHs) from 15 representative Multi-Talkers using 400 real portraits collected online. Through subjective experiments, we analyze perceptual discrepancies among different Multi-Talkers and identify 12 common types of distortion. Furthermore, we introduce EvalTalker, a novel TH quality assessment framework. This framework possesses the ability to perceive global quality, human characteristics, and identity consistency, while integrating Qwen-Sync to perceive multimodal synchrony. Experimental results demonstrate that EvalTalker achieves superior correlation with subjective scores, providing a robust foundation for future research on high-quality Multi-Talker generation and evaluation.","short_abstract":"Speech-driven Talking Human (TH) generation, commonly known as \"Talker,\" currently faces limitations in multi-subject driving capabilities. Extending this paradigm to \"Multi-Talker,\" capable of animating multiple subjects simultaneously, introduces richer interactivity and stronger immersion in audiovisual communicatio...","url_abs":"https://arxiv.org/abs/2512.01340","url_pdf":"https://arxiv.org/pdf/2512.01340v1","authors":"[\"Yingjie Zhou\",\"Xilei Zhu\",\"Siyu Ren\",\"Ziyi Zhao\",\"Ziwen Wang\",\"Farong Wen\",\"Yu Zhou\",\"Jiezhang Cao\",\"Xiongkuo Min\",\"Fengjiao Chen\",\"Xiaoyu Li\",\"Xuezhi Cao\",\"Guangtao Zhai\",\"Xiaohong Liu\"]","published":"2025-12-01T06:56:40Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}