Audio-driven 3D facial animation is essential for advancing immersion and interactivity in virtual experiences. Although recent advances have shown promising capabilities, the training and evaluation of existing methods typically rely on ground-truth-based errors, which fall short of aligning with human preferences. To...
Si-Jing Wu, Yunhao Li, Zhilin Gao et al.· IEEE Transactions on Visuali...· 1 citation
With the rapid advancement of text-to-image (T2I) generation, robust evaluation becomes critical yet challenging, as traditional metrics fail to capture fine-grained alignment and generative artifacts. While large multimodal models (LMMs) are increasingly adopted as evaluators, existing benchmarks typically study seman...
Yu Zhao, Jia-Rui Wang, Hui-Yu Duan et al.· 0 citations
This work proposes LLaVA-Assessor, a unified data construction and model training system for LMM-based machine vision, and introduces a simple yet effective prompt disentanglement strategy to alleviate training-objective confusion in multi-task learning, thereby enabling stable and coherent joint training.
Zi-Heng Jia, Zi-Cheng Zhang, Jia-Ying Qian et al.· 0 citations
This work extends the previous dataset HVEval with pairwise preference annotations and proposes MoE-Rater, a Mixture-of-Experts (MoE)-inspired and multimodal large language model (MLLM)-based all-in-one method that supports multi-dimensional quality rating, multi-dimensional pairwise comparison, and category-specific q...
Si-Jing Wu, Yun-Hao Li, Hui-Yu Duan et al.· IEEE transactions on circuit...· 4 citations
Recent advances in generative video models have enabled camera-controlled world video generation, allowing models to synthesize videos under user-defined camera trajectories. However, existing video quality assessment (VQA) methods are mainly developed for natural videos and fail to capture the unique perceptual charac...
MIE-Bench is introduced, the first large-scale multiple image editing benchmark with fine-grained human preference annotations and MIEScore, a multimodal large language model (MLLM)-based evaluation model enhanced with skill optimization and multi-dimensional supervised fine-tuning, to provide human-aligned feedback fo...
Zi-Tong Xu, Huiyu Duan, Xinyu Zhang et al.· 0 citations
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