Who is a Better Puppeteer: Subjective and Objective Quality Assessment for Generative 3D Animated Human

Abstract

Although recent advances in AI-based Text-to-Motion (T2M) algorithms have enabled text-driven 3D Animated Human (3DAH), often referred to as "Puppeteers" for their ability to streamline 3D animation design, the quality of the generated motion remains a critical concern. More importantly, the T2M field continues to lack reliable and objective evaluation metrics, which substantially hinders the advancement of puppeteers and negatively impacts user experience. To address this gap, we introduce the first large-scale quality assessment dataset for generative 3DAHs, termed AHQA-3D, which comprises 1,200 3DAHs sequences generated by 6 representative puppeteers. Through carefully designed subjective experiments involving recruited participants, we obtain human quality ratings for each 3DAH. The results highlight the impact of different factors on animation quality. We further propose ViSCA, an objective quality assessment agent based on Qwen2.5-VL. ViSCA supports end-to-end quality evaluation across four dimensions: 3D motion fidelity, 2D human body representation, visual quality, and semantic consistency. Experimental results demonstrate that ViSCA outperforms existing evaluation methods and exhibits stronger alignment with human perception.