Emerging text-to-3D generative models based on large diffusion backbones have markedly lowered the barrier to high-quality 3D asset creation, yet rigorous quantitative evaluation remains elusive. We introduce T23D-QA, a novel open benchmark that couples diverse prompts, multiple generation paradigms, and fine-grained human judgements for text-conditioned 3D synthesis. The dataset comprises 1,710 textured meshes produced by nine state-of-the-art pipelines spanning feed-forward, optimization-based, and view-reconstruction families. Assets are driven by a tri-categorical prompt suite: single-object, multi-object, and primitive-anchored, covering 160 ShapeNet-level object classes. Each mesh is rated by 20 participants along three orthogonal dimensions: geometry, texture, and alignment. Building upon this corpus, we propose an evaluator that decouples multi-modal features via cross-attention. On T23D-QA, our baseline surpasses the strongest published metric by 8.1% (geometry), 6.1% (texture), and 1.9% (alignment) in Spearman rank correlation. Dataset and code are publicly available at https://t23d-qa.github.io to foster reproducible research.

AI-Generated Images (AGIs) have inherent mul-timodal nature. Unlike traditional image quality assessment (IQA) on natural scenarios, AGIs quality assessment (AGIQA) takes the correspondence of image and its textual prompt into consideration. This is coupled in the ground truth score, which confuses the unimodal IQA methods. To solve this problem, we introduce IP-IQA (AGIs Quality Assessment via Image and Prompt), a multimodal framework for AGIQA via corresponding image and prompt incorporation. Specifically, we propose a novel incremental pretraining task named Image2Prompt for better understanding of AGIs and their corresponding textual prompts. An effective and efficient image-prompt fusion module, along with a novel special [QA] token, are also applied. Both are plug-and-play and beneficial for the cooperation of image and its corresponding prompt. Experiments demonstrate that our IP-IQA achieves the state-of-the-art on AGIQA-1k and AGIQA-3k datasets. Code will be available at https://github.com/Coobiw/IP-IQA.

Wireless-based federated learning (FL), as an emerging distributed learning approach, has been widely studied for 6G systems. When the paradigm shifts from terrestrial to non-terrestrial networks (NTN), FL may need to address several open challengings, e.g., limited service time of low earth orbit (LEO) satellites and time-efficient uploading and aggregation for massive devices. In this work, we exploit the synergy of LEO and FL for future integrated 6G-satellite systems by taking advantage of ubiquitous wireless access provided by LEO and appealing characteristics of collaborative training and data privacy preservation in FL. The studied LEO-FL framework may need to improve multi-metric performance in practice. Different from most FL works, we simultaneously improve the communication-training efficiency and local training accuracy from a multi-objective optimization (MOO) perspective. To solve the problem, we propose a decomposition, deep reinforcement learning and transfer learning based MOO algorithm for FL (DRT-FL), aiming at adapting to the dynamic satellite-terrestrial environments, achieving efficient uploading and aggregation, and approaching Pareto optimal sets. Compared to the state-of-the-art MOO algorithms, the effectiveness of the proposed LEO-FL framework and DRT-FL algorithm are assessed on MNIST and CIFAR-IO datasets.