通过直接操作模型权重空间，实现对3D形状拓扑与局部特征的精准控制。
Shape Generation via Weight Space Learning
- 将3D生成模型的权重空间视为可直接探索的数据域，发现其子流形可分别调控拓扑与细节。
- 在条件空间插值时出现全局连通性突变，表明微小权重变化可显著改变形状结构。
- 低维重参数化可在极少数据下实现局部几何的可控调整，适合精细化定制场景。
用于3D形状生成的基础模型近期展现出强大的全局与局部几何先验能力。然而，由于真实数据常稀缺或噪声大，传统微调易引发灾难性遗忘。本文将大型3D形状生成模型的权重空间视为可直接探索的数据模态。我们假设该高维权重空间中的子流形可分别调节拓扑属性或细粒度部件特征，并通过两项实验提供初步证据：首先，在条件空间插值时观察到全局连通性的急剧相变，表明权重空间的微小变化可大幅改变拓扑；其次，低维重参数化即使在极少量数据下也能实现可控的局部几何变化。这些结果揭示了权重空间学习在3D形状生成与专用微调中的新可能。
原文摘要 · Abstract (English)
Foundation models for 3D shape generation have recently shown a remarkable capacity to encode rich geometric priors across both global and local dimensions. However, leveraging these priors for downstream tasks can be challenging as real-world data are often scarce or noisy, and traditional fine-tuning can lead to catastrophic forgetting. In this work, we treat the weight space of a large 3D shape-generative model as a data modality that can be explored directly. We hypothesize that submanifolds within this high-dimensional weight space can modulate topological properties or fine-grained part features separately, demonstrating early-stage evidence via two experiments. First, we observe a sharp phase transition in global connectivity when interpolating in conditioning space, suggesting that small changes in weight space can drastically alter topology. Second, we show that low-dimensional reparameterizations yield controlled local geometry changes even with very limited data. These results highlight the potential of weight space learning to unlock new approaches for 3D shape generation and specialized fine-tuning.
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