Yingjie Zhou

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.

Web3 technologies enable novel forms of real-time digital human streaming media by supporting both high-fidelity transmission and interactive user engagement. However, Real-Time Streaming Interactive Digital Humans (RTSIDHs) remain vulnerable to network instability, resulting in buffering, latency, visual degradation, and audio–video desynchronization that substantially impair user Quality of Experience (QoE). To effectively perceive these distortions, we present RDHQA, the first large-scale RTSIDH Quality Assessment dataset. RDHQA comprises 134 representative interaction scenarios with eight digital human avatars as high-quality references, along with 1,340 distorted samples generated by simulating five common streaming degradations. Based on extensive subjective evaluations, we further propose SAV-PF, an audio–visual quality assessment method built on the human foundation model Sapiens and informed by cognitive principles such as the primacy effect and forgetting curve. Experimental results demonstrate that SAV-PF achieves superior performance over existing objective QoE assessment approaches, providing a more accurate prediction of user experience. This work is open-sourced at https://github.com/zyj-2000/RDHQA under the CC BY-NC 4.0 licence.

Surveillance facial images are often captured under unconstrained conditions, resulting in severe quality degradation due to factors such as low resolution, motion blur, occlusion, and poor lighting. Although recent face restoration techniques applied to surveillance cameras can significantly enhance visual quality, they often compromise fidelity (i.e., identity-preserving features), which directly conflicts with the primary objective of surveillance images—reliable identity verification. Existing facial image quality assessment (FIQA) predominantly focus on either visual quality or recognition-oriented evaluation, thereby failing to jointly address visual quality and fidelity, which are critical for surveillance applications. To bridge this gap, we propose the first comprehensive study on surveillance facial image quality assessment (SFIQA), targeting the unique challenges inherent to surveillance scenarios. Specifically, we first construct SFIQA-Bench, a multi-dimensional quality assessment benchmark for surveillance facial images, which consists of 5, 004 surveillance facial images captured by three widely deployed surveillance cameras in real-world scenarios. A subjective experiment is conducted to collect six dimensional quality ratings, including noise, sharpness, colorfulness, contrast, fidelity and overall quality, covering the key aspects of SFIQA. Furthermore, we propose SFIQA-Assessor, a lightweight multi-task FIQA model that jointly exploits complementary facial views through cross-view feature interaction, and employs learnable task tokens to guide the unified regression of multiple quality dimensions. The experiment results on the proposed dataset show that our method achieves the best performance compared with the state-of-the-art general image quality assessment (IQA) and FIQA methods, validating its effectiveness for real-world surveillance applications. Both SFIQA-Bench and SFIQA-Assessor will be publicly released.

Quality assessment, which evaluates the visual quality level of multimedia experiences, has garnered significant attention from researchers and has evolved substantially through dedicated efforts. Before the advent of large models, quality assessment typically relied on small expert models tailored for specific tasks. While these smaller models are effective at handling their designated tasks and predicting quality levels, they often lack explainability and robustness. With the advancement of large models, which align more closely with human cognitive and perceptual processes, many researchers are now leveraging the prior knowledge embedded in these large models for quality assessment tasks. This emergence of quality assessment within the context of large models motivates us to provide a comprehensive review focusing on two key aspects: (1) the assessment of large models and (2) the role of large models in assessment tasks. We begin by reflecting on the historical development of quality assessment. Subsequently, we move to detailed discussions of related works concerning quality assessment in the era of large models. Finally, we offer insights into the future progression and potential pathways for quality assessment in this new era. We hope that this survey will enable a rapid understanding of the development of quality assessment in the era of large models and inspire further advancements in the field.

Animated human (AH) have gained popularity due to their vivid appearance and smooth, natural movements. Various animation methods based on artificial intelligence (AI) have been introduced, which are viewed as “Imitators,” offering new solutions for designing AHs. However, the effectiveness of these AI-generated AHs varies significantly across different categories and within the same category, leading to visual distortions that adversely affect the viewer’s experience. Consequently, it is essential to evaluate the quality of AHs to provide reliable and objective indicators for their further development and to ensure the delivery of higher-quality AH videos to users. In this paper, the first Animated Human Quality Assessment (AHQA) dataset is constructed by selecting 6 advanced and popular imitators and 10 common actions to animate 20 AI-generated characters. The constructed dataset integrates different genders and age groups of character images, and two types of poses, standing and sitting, are selected, highlighting the comprehensiveness and diversity of the AHQA dataset. Subjective experiments reveal significant differences in the quality of AHs produced by different imitators. Finally, we propose a quality assessment method, VIP-QA, incorporating Video quality, Identity consistency, and Posture similarity for the AHQA dataset. Experimental results show that VIP-QA significantly outperforms existing assessment methods on multiple datasets by about 5%, more closely approximates human visual perception, and provides a valid objective metric for assessing imitators. All the work in this paper has been released at https://github.com/zyj-2000/Imitator.