LatentReRig：一种基于SDF的双解码器变分自编码器，用于潜空间变形条件化
LatentReRig: An SDF-Based VAE with Dual Decoders for Latent-Space Deformation Conditioning
AI总结：
LatentReRig提出基于SDF的变分自编码器，利用双解码器在潜空间学习可复用的姿态变形方向，实验表明可指导未见角色变形，但局部变化预测需改进。
AI中文摘要：
在不同几何形状和拓扑结构的角色之间传递变形具有挑战性，因为传统绑定通过角色特定的结构和对应关系来编码行为。我们提出了LatentReRig，一个实验性框架，旨在研究姿态相关的变化是否可以被表示为学习到的几何潜空间中的可复用方向。该框架采用基于符号距离函数（SDF）的变分自编码器，并耦合两个解码器：一个重建隐式场，另一个从源几何和潜变形条件化预测目标顶点位置。源几何可以是中性的或已经变形的。在受控的人形数据集上的实验表明，多种姿态在身份间诱导出连贯的潜方向，尤其是对于广泛的关节运动。这些信号可以指导未见角色的变形，但对于局部变化和修正贡献，显式预测仍不够准确。与重复SDF采样的诊断比较显示，身份间距离平均超过同几何重采样变异性，而姿态信号在此基线上表现出不同的余量。结果支持可复用姿态相关结构的存在，并确定稳定的局部条件化和精确的网格解码是改进传递的互补要求。
英文摘要：
Transferring deformation between characters with different geometry and topology is challenging because conventional rigs encode behaviour through character-specific structures and correspondences. We present LatentReRig, an experimental framework that investigates whether pose-associated changes can instead be represented as reusable directions in a learned geometric latent space. An SDF-based variational autoencoder is coupled with two decoders: one reconstructs the implicit field, while the other predicts target vertex positions from source geometry and latent deformation conditioning. The source geometry may be neutral or already deformed. Experiments on a controlled humanoid dataset show that several poses induce coherent latent directions across identities, particularly for broad articulated motions. These signals can guide deformation of unseen characters, but explicit predictions remain less accurate for localized changes and corrective contributions. Diagnostic comparisons with repeated SDF sampling show that inter-identity distances exceed same-geometry resampling variability on average, while pose signals exhibit different margins above this baseline. The results support the presence of reusable pose-related structure and identify stable local conditioning and accurate mesh decoding as complementary requirements for improving transfer.
