ViLearn：通过可见性学习加速图像到三维生成的训练收敛
ViLearn: Accelerating Training Convergence of Image-to-3D Generation via Visibility Learning
将 VecSet 词元按可见性分组并设计可见性感知位置编码，为单图三维生成中的扩散模型注入结构先验，可显著加速收敛；该思路可迁移到其他基于无序集合表示的三维生成或重建任务。
原文依据 Visibility-Aware Positional Encoding (VAPE), which assigns shared positional embeddings to image tokens and visible shape tokens
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论单图到三维形状生成已取得显著进展，这得益于在三维变分自编码器压缩潜空间上训练的潜扩散模型。然而，该任务本质上是病态的：从单一视角恢复完整的三维几何——尤其是被遮挡的表面——本身具有歧义。现有的基于 VecSet 的方法将形状词元视为无序集合且缺乏显式位置编码，进一步加剧了这一挑战。这种设计迫使扩散模型同时从输入图像学习可见对应关系，并在一个大的、排列不变且缺乏结构的词元空间中幻觉出不可见几何，显著阻碍了训练效率和收敛稳定性。为此，我们提出可见性学习，一种向图像到三维流程注入可见性结构和位置归纳偏置的训练范式。我们的方法包含两个协同组件：(1) 可见性分组（VG），利用 VecSet 变分自编码器解码器的空间局部性，显式地将 VecSet 词元划分为可见和不可见子集；(2) 可见性感知位置编码（VAPE），为图像词元和可见形状词元分配共享的位置嵌入以增强其对应关系，同时为不可见词元使用不同的编码以引导幻觉。通过显式解耦可见重建与不可见幻觉，我们的方法缩小了有效假设空间，并为扩散模型提供了清晰的结构指导。大量实验表明，可见性学习将训练收敛速度提升高达 4.4 倍，同时相比强 VecSet 基线取得了更优的生成质量。
英文原文摘要 1756 字符
Single-image-to-3D shape generation has seen remarkable progress, driven by latent diffusion models trained on the compressed latent space of 3D VAEs. However, the task remains intrinsically ill-posed: recovering complete 3D geometry--especially occluded surfaces--from a single view is inherently ambiguous. Existing VecSet-based approaches further exacerbate this challenge by treating shape tokens as an unordered set without explicit positional encoding. This design forces diffusion models to simultaneously learn visible correspondences from the input image and hallucinate invisible geometry within a large, permutation-invariant token space, where the lack of structure significantly hinders training efficiency and convergence stability.To address this, we propose Visibility Learning, a training paradigm that injects visibility structure and positional inductive bias into the image-to-3D pipeline. Our method comprises two synergistic components: (1) Visibility Grouping (VG), which explicitly partitions VecSet tokens into visible and invisible subsets by exploiting the spatial locality of VecSet VAE decoders; and (2) Visibility-Aware Positional Encoding (VAPE), which assigns shared positional embeddings to image tokens and visible shape tokens to amplify their correspondence, while using distinct encodings for invisible tokens to guide hallucination. By explicitly disentangling visible reconstruction from invisible hallucination, our approach shrinks the effective hypothesis space and provides clear structural guidance for diffusion models. Extensive experiments demonstrate that Visibility Learning accelerates training convergence by up to 4.4xwhile achieving superior generation quality compared to strong VecSet-based baselines.