HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluation

• URL: http://arxiv.org/abs/2604.25361v1
• Date: Tue, 28 Apr 2026 08:27:35 GMT
• Title: HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluation
• Abstract summary: We propose HuM-Eval, a novel human-centric evaluation framework.<n>HuM-Eval achieves an average human correlation of 58.2%, outperforming state-of-the-art baselines.
• Score: 15.568587059061025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video generation models have developed rapidly in recent years, where generating natural human motion plays a pivotal role. However, accurately evaluating the quality of generated human motion video remains a significant challenge. Existing evaluation metrics primarily focus on global scene statistics, often overlooking fine-grained human details and consequently failing to align with human subjective preference. To bridge this gap, we propose HuM-Eval, a novel human-centric evaluation framework that adopts a coarse-to-fine strategy. Specifically, our framework first utilizes a Vision Language Model to perform a coarse assessment of global video quality. It then proceeds to a fine-grained analysis, using 2D pose to verify anatomical correctness and 3D human motion to evaluate motion stability. Extensive experiments demonstrate that HuM-Eval achieves an average human correlation of 58.2%, outperforming state-of-the-art baselines. Furthermore, we introduce HuM-Bench, a comprehensive benchmark comprising 1,000 diverse prompts, and conduct a detailed evaluation of existing text-to-video models, paving the way for next-generation human motion generation.

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