2024年12月17日 · Using the FaceQ database, we establish F-Bench, a benchmark for comparing and evaluating face generation, customization, and restoration models, highlighting strengths and weaknesses across various prompts and evaluation dimensions.

Using the FaceQ database, we establish F-Bench, a benchmark for comparing and evaluating face generation, customization, and restoration models, highlighting strengths and weaknesses across various prompts and evaluation dimensions. Additionally, we assess the performance of existing image quality assessment (IQA), face quality assessment (FQA ...

FurMark 2 is the successor of the venerable FurMark 1 and is a very intensive GPU stress test on Windows (32-bit and 64-bit) and Linux (32-bit and 64-bit) platforms. It's also a quick OpenGL and Vulkan graphics benchmark with online scores. FurMark 2 has an improved command line support and is built with GeeXLab.

4 F-Bench: Benchmarking Current Generative Face Models. 4.1 Perspective Analysis; 4.2 Model-wise Comparison. Face Generation. Face Customization. Face Restoration. 4.3 Class-wise Comparison; 5 Performance Analysis on Current Quality Assessment Methods. 5.1 Dataset and Benchmark Models. Traditional IQA Models.

FAHBench runs a sample molecular dynamics calculation to benchmark GPUs as it pertains to biophysics research. FAHBench performs the same computations as those found on Folding@Home. This set of releases uses OpenMM 6.2 corresponding to FAH core 0x21. It uses fah patches corresponding to version 0.0.18 of core 0x21.

2024年12月17日 · New benchmark called F-Bench for evaluating AI face generation and editing; Measures human preference alignment in facial image tasks; Covers 3 key areas: generation, customization, and restoration; Contains 9,000 carefully curated image pairs; Introduces novel scoring metrics based on human preferences; Plain English Explanation

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2025年1月28日 · Bibliographic details on F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration.

2024年12月18日 · The paper introduces the FaceQ database of 12,255 AI-generated face images with human preference annotations and establishes F-Bench, a benchmarking framework to improve evaluation metrics for face generation, customization, and restoration models, addressing issues of authenticity and identity fidelity.

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