12 papers
EvalTalker: Learning to Evaluate Real-Portrait-Driven Multi-Subject Talking Humans
Yingjie Zhou, Xilei Zhu, Siyu Ren +11
Speech-driven Talking Human (TH) generation, commonly known as "Talker," currently faces limitations in multi-subject driving capabilities. Extending this paradigm to "Multi-Talker…
Q-Mirror: Unlocking the Multi-Modal Potential of Scientific Text-Only QA Pairs
Junying Wang, Zicheng Zhang, Ye Shen +8
High-quality, multi-modal benchmarks are crucial for advancing scientific reasoning in large models yet their manual creation is costly and unscalable. To address this bottleneck,…
A Multi-To-One Interview Paradigm for Efficient MLLM Evaluation
Ye Shen, Junying Wang, Farong Wen +4
The rapid progress of Multi-Modal Large Language Models (MLLMs) has spurred the creation of numerous benchmarks. However, conventional full-coverage Question-Answering evaluations…
Who is a Better Player: LLM against LLM
Yingjie Zhou, Jiezhang Cao, Farong Wen +10
Adversarial board games, as a paradigmatic domain of strategic reasoning and intelligence, have long served as both a popular competitive activity and a benchmark for evaluating ar…
Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads
Yingjie Zhou, Jiezhang Cao, Zicheng Zhang +6
Speech-driven methods for portraits are figuratively known as "Talkers" because of their capability to synthesize speaking mouth shapes and facial movements. Especially with the ra…
The Ever-Evolving Science Exam
Junying Wang, Zicheng Zhang, Yijin Guo +9
As foundation models grow rapidly in capability and deployment, evaluating their scientific understanding becomes increasingly critical. Existing science benchmarks have made progr…