Vanilla text-to-image diffusion models struggle with generating accurate
human images, commonly resulting in imperfect anatomies such as unnatural
postures or disproportionate limbs.Existing methods address this issue mostly
by fine-tuning the model with extra images or adding additional controls --
human-centric priors such as pose or depth maps -- during the image generation
phase. This paper explores the integration of these human-centric priors
directly into the model fine-tuning stage, essentially eliminating the need for
extra conditions at the inference stage. We realize this idea by proposing a
human-centric alignment loss to strengthen human-related information from the
textual prompts within the cross-attention maps. To ensure semantic detail
richness and human structural accuracy during fine-tuning, we introduce
scale-aware and step-wise constraints within the diffusion process, according
to an in-depth analysis of the cross-attention layer. Extensive experiments
show that our method largely improves over state-of-the-art text-to-image
models to synthesize high-quality human images based on user-written prompts.
Project page: https://hcplayercvpr2024.github.io.

该研究论文探索将人类中心先验直接整合到模型微调阶段，通过人类中心对齐损失强化文本提示中的人类相关信息，以及通过扩散过程中的比例感知和逐步约束确保语义详细性和人类结构准确性，从而提高了合成基于用户编写提示的高质量人类图像的方法。

文本为基础的人类图像生成中人本先验在扩散模型中的有效应用

Towards Effective Usage of Human-Centric Priors in Diffusion Models for  Text-based Human Image Generation

Image registration is a fundamental medical image analysis task, and a wide
variety of approaches have been proposed. However, only a few studies have
comprehensively compared medical image registration approaches on a wide range
of clinically relevant tasks. This limits the development of registration
methods, the adoption of research advances into practice, and a fair benchmark
across competing approaches. The Learn2Reg challenge addresses these
limitations by providing a multi-task medical image registration data set for
comprehensive characterisation of deformable registration algorithms. A
continuous evaluation will be possible at
this https URL Learn2Reg covers a wide range of
anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR),
availability of annotations, as well as intra- and inter-patient registration
evaluation. We established an easily accessible framework for training and
validation of 3D registration methods, which enabled the compilation of results
of over 65 individual method submissions from more than 20 unique teams. We
used a complementary set of metrics, including robustness, accuracy,
plausibility, and runtime, enabling unique insight into the current
state-of-the-art of medical image registration. This paper describes datasets,
tasks, evaluation methods and results of the challenge, as well as results of
further analysis of transferability to new datasets, the importance of label
supervision, and resulting bias. While no single approach worked best across
all tasks, many methodological aspects could be identified that push the
performance of medical image registration to new state-of-the-art performance.
Furthermore, we demystified the common belief that conventional registration
methods have to be much slower than deep-learning-based methods.

本文描述了 Learn2Reg 挑战赛的数据集，任务，评估方法和结果，以及结果的进一步分析。同时，该研究也揭示出许多提高医学图像配准表现的方法，从而推动了该领域的发展。