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.

Although 3D generated content (3DGC) offers advantages in reducing production costs and accelerating design timelines, its quality often falls short when compared to 3D professionally generated content. Common quality issues frequently affect 3DGC, highlighting the importance of timely and effective quality assessment. Such evaluations not only ensure a higher standard of 3DGCs for end-users but also provide critical insights for advancing generative technologies. To address existing gaps in this domain, this paper introduces a novel 3DGC quality assessment dataset, 3DGCQA, built using 7 representative Text-to-3D generation methods. During the dataset’s construction, 50 fixed prompts are utilized to generate contents across all methods, resulting in the creation of 313 textured meshes that constitute the 3DGCQA dataset. The visualization intuitively reveals the presence of 6 common distortion categories in the generated 3DGCs. To further explore the quality of the 3DGCs, subjective quality assessment is conducted by evaluators, whose ratings reveal significant variation in quality across different generation methods. Additionally, several objective quality assessment algorithms are tested on the 3DGCQA dataset. The results expose limitations in the performance of existing algorithms and underscore the need for developing more specialized quality assessment methods. To provide a valuable resource for future research and development in 3D content generation and quality assessment, the dataset has been open-sourced in https://github.com/zyj-2000/3DGCQA.

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 rapid development of the Text-to-Image (T2I) models, AI-Generated Talking Heads (AGTHs) have gradually become an emerging digital human media. However, challenges persist regarding the quality of these talkers and AGTHs they generate, and comprehensive studies addressing these issues remain limited. To address this gap, this paper presents the largest AGTH quality assessment dataset THQA-10K to date, which selects 12 prominent T2I models and 14 advanced talkers to generate AGTHs for 14 prompts. After excluding instances where AGTH generation is unsuccessful, the THQA-10K dataset contains 10,457 AGTHs. Then, volunteers are recruited to subjectively rate the AGTHs and give the corresponding distortion categories. In our analysis for subjective experimental results, we evaluate the performance of talkers in terms of generalizability and quality, and also expose the distortions of existing AGTHs. Finally, an objective quality assessment method based on the first frame, Y-T slice and tone-lip consistency is proposed. Experimental results show that this method can achieve state-of-the-art (SOTA) performance in AGTH quality assessment. The work is released at https://github.com/zyj-2000/Talker.

Nowadays, personal privacy protection is extremely emphasised. Face de-identification is considered as an effective way to protect the visual privacy through disguising or replacing identity attributes. Existing methods compromise either high fidelity or reversibility. To address these issues, this paper proposes DiffDeid, the first diffusion-based face de-identification and recovery method. Leveraging recent diffusion inversion and control technicques, DiffDeid achieves both high quality imperceptible de-identification and exact recovery with passwords. DiffDeid has three attractions: (1) It can generate de-identified faces with the superior fidelity while maintaining other non-identity attributes for visual tasks. (2) The correct password is powerful enough to restore facial images with extreme details. Meanwhile, incorrect passwords can lead to vastly different decryption results. (3) DiffDeid demands minimal computing resources and instant training time compared to others. We conducted experiments on various face datasets to showcase the superiority of our proposed method. Additional experiments show that DiffDeid is powerful with diverse text prompts and control instructions even beyond human faces. Codes are available at project page.

The advent of generative AI has revolutionized 3D content design, significantly enhancing modelers’ efficiency. However, the quality of generated 3D content, particularly Generated Meshes (GMs), remains a critical concern. GMs pose unique challenges for quality assessment due to their complex geometry, detailed texture mapping, and distortions that differ from traditional meshes. Existing methods fail to address these GM-specific issues. To tackle this gap, we introduce a novel no-reference quality assessment method, CAP, which integrates CT-Slice, prompt Alignment, and Projections. CAP employs a six-face projection to capture external features and a CT-like slicing approach to extract internal quality features. Additionally, it leverages Contrastive Language-Image Pre-Training (CLIP) to measure the alignment between projection embeddings and prompts as a key quality indicator. Experimental results demonstrate that CAP effectively evaluates GM quality by combining internal, external, and alignment features. The code for this work has been open-sourced in https://github.com/zyj-2000/CAP.

Human-centric videos play a significant role in the pervasive video content of modern life. However, the capabilities of text-to-video (T2V) generation models and video-to-text (V2T) understanding models for human-centric videos remain largely unexplored. To this end, we present HVEval, the first comprehensive evaluation dataset focusing on human-centric videos, which consists of 20,000 videos, 60k MOS annotations across 3 dimensions (i.e., spatial quality, temporal quality, and text-video correspondence), and 20k category-specific Q&A pairs. Based on the HVEval dataset, this paper aims to answer three questions: (1) can today's T2V models effectively generate human-centric videos following the given prompts? (2) how effective are today's V2T LMMs in understanding and evaluating human-centric videos? (3) are current VQA metrics good enough for evaluating human-centric videos? Comprehensive evaluations of 24 T2V models, 20 LMMs, and 18 VQA metrics reveal their limitations in fine-grained text-controlled generation and human-aligned perception and understanding, highlighting the significant potential of our dataset and benchmarks to advance research in human-centric video generation and understanding.

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.

Traditional deep neural network (DNN)-based image quality assessment (IQA) models leverage convolutional neural networks (CNN) or Transformer to learn the quality-aware feature representation, achieving commendable performance on natural scene images. However, when applied to AI-Generated images (AGIs), these DNN-based IQA models exhibit subpar performance. This situation is largely due to the semantic inaccuracies inherent in certain AGIs caused by uncontrollable nature of the generation process. Thus, the capability to discern semantic content becomes crucial for assessing the quality of AGIs. Traditional DNN-based IQA models, constrained by limited parameter complexity and training data, struggle to capture complex fine-grained semantic features, making it challenging to grasp the existence and coherence of semantic content of the entire image. To address the shortfall in semantic content perception of current IQA models, we introduce a large Multi-modality model Assisted AI-Generated Image Quality Assessment (MA-AGIQA) model, which utilizes semantically informed guidance to sense semantic information and extract semantic vectors through carefully designed text prompts. Moreover, it employs a mixture of experts (MoE) structure to dynamically integrate the semantic information with the quality-aware features extracted by traditional DNN-based IQA models. Comprehensive experiments conducted on two AI-generated content datasets and two traditional IQA datasets show that MA-AGIQA achieves state-of-the-art performance, and demonstrate its superior generalization capabilities on assessing the quality of AGIs. The code is available at https://github.com/wangpuyi/MA-AGIQA.

Based on the fact that both the phase and amplitude characteristics of fractional capacitor are related to its order, the introduction of fractional capacitor will make power factor correction (PFC) more flexible compared with the one using conventional capacitor. In order to verify the PFC function with fractional capacitor, an equivalent fractional capacitor model composed of an inverter and a resistor in series is presented in this paper. As the inverter is served as a controlled voltage source, the order of the proposed fractional capacitor can be varied between 0 and 2 easily just by changing the control parameters of the inverter, and the power level of the fractional capacitor model is the same as that of the inverter, which is able to be used in high power occasion. Finally, the simulation and experimental results are provided to validate the feasibility of power factor corrector with the proposed fractional capacitor model.