Evaluating text-to-image generative models remains a challenge, despite the remarkable progress being made in their overall performances. While existing metrics like CLIPScore work for coarse evaluations, they lack the sensitivity to distinguish finer differences as model performance rapidly improves. In this work, we focus on the text rendering aspect of these models, which provides a lens for evaluating a generative model's fine-grained instruction-following capabilities. To this end, we introduce a new evaluation framework called TypeScore to sensitively assess a model's ability to generate images with high-fidelity embedded text by following precise instructions. We argue that this text generation capability serves as a proxy for general instruction-following ability in image synthesis. TypeScore uses an additional image description model and leverages an ensemble dissimilarity measure between the original and extracted text to evaluate the fidelity of the rendered text. Our proposed metric demonstrates greater resolution than CLIPScore to differentiate popular image generation models across a range of instructions with diverse text styles. Our study also evaluates how well these vision-language models (VLMs) adhere to stylistic instructions, disentangling style evaluation from embedded-text fidelity. Through human evaluation studies, we quantitatively meta-evaluate the effectiveness of the metric. Comprehensive analysis is conducted to explore factors such as text length, captioning models, and current progress towards human parity on this task. The framework provides insights into remaining gaps in instruction-following for image generation with embedded text.
Generative AI-Driven High-Fidelity Human Motion Simulation
The Polytechnic School, Ira A. Fulton Schools of Engineering, Arizona State University（亚利桑那州立大学工程学院Polytechnic学院）；School of Manufacturing Systems and Networks, Ira A. Fulton Schools of Engineering, Arizona State University（亚利桑那州立大学工程学院制造系统与网络学院）
本文提出 G-AI-HMS 框架，通过整合大型语言模型与文本到运动模型生成运动感知描述，并利用计算机视觉验证 AI 增强运动与真实人体运动的一致性，在八项工业任务中显著降低关节误差和时间错位。
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
Machine Learning Group, CITEC Bielefeld University Germany（比勒菲尔德大学机器学习小组、CITEC）；Institute of Mathematics Technical University Berlin Germany（柏林技术大学数学研究所）
FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation
Northeastern University（东北大学）；University of Alberta（阿尔伯塔大学）；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing（高性能计算研究所）
UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Information and Communication, Institute of Technology of Cambodia（柬埔寨技术学院信息与通信系）；Department of Telecommunication and Network Engineering, Institute of Technology of Cambodia（柬埔寨技术学院电信与网络工程系）
Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction
通过利用深度卷积神经网络（DCNN）的能力，已经做了大量的工作来从单个图像重建3D面部结构。在最近的作品中，纹理特征要么对应于线性纹理空间的分量，要么由自动编码器直接从野外图像中学习。在所有情况下，面部纹理重建的质量仍然不能建模面部纹理与高频细节。在本文中，我们采取了一种完全不同的方法，利用生成对抗网络（GANs）和DCNN的强大功能，从单张图像中重建面部纹理和形状。也就是说，我们利用GANs在\edit{from a large-scale 3D texture dataset}之前训练一个非常强大的面部纹理。然后，我们重新审视原始的3D变形模型（3DMMs）拟合利用非线性优化，以找到最佳的潜在参数，最好地重建测试图像，但在一个新的角度。为了对初始化具有鲁棒性并加快拟合过程，我们提出了一种新的基于自监督回归的方法。我们在真实感和身份保持3D人脸重建中表现出出色的效果，并首次实现了高频细节的面部纹理重建。
G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors
South China University of Technology（华南理工大学）；Guangdong Engineering Center for Large Model and GenAI Technology（广东省大模型与生成式人工智能技术工程中心）；State Key Laboratory of Subtropical Building and Urban Science（亚热带建筑与城市科学国家重点实验室）；The Hong Kong Polytechnic University（香港理工大学）
Generative Drifting for Conditional Medical Image Generation
Department of Orthodontics and Orofacial Orthopedics, Friedrich-Alexander-University Erlangen-Nuremberg（口腔正畸与面部骨科系，弗里德里希-艾萨克-埃尔兰-纽伦堡大学）；Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nuremberg（生物医学工程人工智能系，弗里德里希-艾萨克-埃尔兰-纽伦堡大学）；Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg（模式识别实验室，弗里德里希-艾萨克-埃尔兰-纽伦堡大学）；Department of Biomedical Engineering, Sun-Yat-sen University（生物医学工程系，孙中山大学）
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Platform and Content Group (PCG), Tencent（腾讯平台与内容事业群）；Shanghai AI Lab（上海人工智能实验室）
PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Artificial Intelligence, Beijing Normal University（人工智能学院，北京师范大学）
CtrlNeRF: The Generative Neural Radiation Fields for the Controllable Synthesis of High-fidelity 3D-Aware Images
School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Department of Electrical and Computer Engineering, California State Polytechnic University（加州州立理工大学波莫纳分校电气与计算机工程学院）
NeuroGAN-3D: Enhancing Intrinsic Functional Brain Networks via High-Fidelity 3D Generative Super-Resolution
Georgia State University（佐治亚州立大学）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（神经影像与数据科学转化研究三机构中心（TReNDS））；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity
Graz University of Technology Austria（奥地利格拉茨理工大学）；Tampere University Finland（芬兰塔尔库大学）；Technical University of Munich Germany（德国慕尼黑技术大学）；Stanford University The United States of America（美国斯坦福大学）；Xi’an Jiaotong University China（中国西安交通大学）；École des Ponts ParisTech France（法国巴黎综合理工学院）
GBR: Generative Bundle Refinement for High-fidelity Gaussian Splatting with Enhanced Mesh Reconstruction
College of future information technology, Fudan University（未来信息科技学院，复旦大学）；School of Biomedical Engineering, Tsinghua University（生物医学工程学院，清华大学）；Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University（电磁波信息科学重点实验室（MoE），复旦大学）；Department of Automation, Tsinghua University（自动化系，清华大学）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（人工智能重点实验室，人工智能研究院，上海交通大学）
