标题： AniMer+：通过家族感知变压器在哺乳动物和鸟类中统一姿态和形状估计 显示英文标题

标题： AniMer+: Unified Pose and Shape Estimation Across Mammalia and Aves via Family-Aware Transformer

摘要： 在基础模型的时代，通过单一网络实现对不同动态物体的统一理解，有可能增强更强的空间智能。 此外，在生物研究中，对不同物种的动物姿态和形状进行准确估计对于定量分析至关重要。 然而，由于之前方法的网络容量有限以及全面多物种数据集的稀缺，这一主题仍研究不足。 为解决这些限制，我们引入了AniMer+，这是我们可扩展的AniMer框架的扩展版本。 在本文中，我们专注于重建哺乳动物（哺乳纲）和鸟类（鸟纲）的统一方法。 AniMer+的一个关键创新是其高容量、家族感知的视觉变换器（ViT），结合了专家混合（MoE）设计。 其架构将网络层划分为分类群特定组件（针对哺乳动物和鸟类）和分类群共享组件，使单一模型中能够高效学习独特的和共同的解剖特征。 为克服3D训练数据的严重短缺，尤其是鸟类的数据，我们引入了一个基于扩散的条件图像生成流程。 该流程生成两个大规模的合成数据集：CtrlAni3D用于四足动物，CtrlAVES3D用于鸟类。 需要注意的是，CtrlAVES3D是第一个大规模的、3D标注的鸟类数据集，这对于解决单视角深度歧义至关重要。 在包含41.3k哺乳动物和12.4k鸟类图像（结合真实和合成数据）的聚合数据集上进行训练，我们的方法在广泛的基准测试中表现出优于现有方法的性能，包括具有挑战性的跨领域Animal Kingdom数据集。 消融研究表明，我们新颖的网络架构和生成的合成数据集在提高现实应用性能方面是有效的。 显示英文摘要

摘要： In the era of foundation models, achieving a unified understanding of different dynamic objects through a single network has the potential to empower stronger spatial intelligence. Moreover, accurate estimation of animal pose and shape across diverse species is essential for quantitative analysis in biological research. However, this topic remains underexplored due to the limited network capacity of previous methods and the scarcity of comprehensive multi-species datasets. To address these limitations, we introduce AniMer+, an extended version of our scalable AniMer framework. In this paper, we focus on a unified approach for reconstructing mammals (mammalia) and birds (aves). A key innovation of AniMer+ is its high-capacity, family-aware Vision Transformer (ViT) incorporating a Mixture-of-Experts (MoE) design. Its architecture partitions network layers into taxa-specific components (for mammalia and aves) and taxa-shared components, enabling efficient learning of both distinct and common anatomical features within a single model. To overcome the critical shortage of 3D training data, especially for birds, we introduce a diffusion-based conditional image generation pipeline. This pipeline produces two large-scale synthetic datasets: CtrlAni3D for quadrupeds and CtrlAVES3D for birds. To note, CtrlAVES3D is the first large-scale, 3D-annotated dataset for birds, which is crucial for resolving single-view depth ambiguities. Trained on an aggregated collection of 41.3k mammalian and 12.4k avian images (combining real and synthetic data), our method demonstrates superior performance over existing approaches across a wide range of benchmarks, including the challenging out-of-domain Animal Kingdom dataset. Ablation studies confirm the effectiveness of both our novel network architecture and the generated synthetic datasets in enhancing real-world application performance.