Abstract
3D morphing remains challenging due to the difficulty of generating semantically consistent and temporally smooth deformations, especially across categories. We present MorphAny3D, a training-free framework that leverages Structured Latent (SLAT) representations for high-quality 3D morphing. Our key insight is that intelligently blending source and target SLAT features within the attention mechanisms of 3D generators naturally produces plausible morphing sequences. To this end, we introduce Morphing Cross-Attention (MCA), which fuses source and target information for structural coherence, and Temporal-Fused Self-Attention (TFSA), which enhances temporal consistency by incorporating features from preceding frames. An orientation correction strategy further mitigates the pose ambiguity within the morphing steps. Extensive experiments show that our method generates state-of-the-art morphing sequences, even for challenging cross-category cases. MorphAny3D further supports advanced applications such as decoupled morphing and 3D style transfer, and can be generalized to other SLAT-based generative models. Project page: this https URL.
Abstract (translated)
3D变形（morphing）仍然面临挑战，主要是因为生成具有语义一致性和时间上平滑的形变非常困难，尤其是在跨类别的情况下。我们提出了MorphAny3D，这是一个无需训练的框架，它利用结构化潜在（SLAT）表示来实现高质量的3D变形。我们的关键见解是，在3D生成器的注意力机制中智能地混合源和目标SLAT特征能够自然产生合理的形变序列。 为此，我们引入了形变交叉注意（MCA），该技术融合源和目标信息以确保结构的一致性，并且引入时间融合自关注（TFSA）来通过加入前一帧的特性增强时间上的连续性。一种方向校正策略进一步减轻变形步骤中的姿态模糊问题。 大量的实验表明，我们的方法可以生成业界领先的形变序列，甚至在具有挑战性的跨类别情况下也是如此。MorphAny3D还支持更高级的应用程序，如解耦变形和3D风格转换，并且可以扩展应用于其他基于SLAT的生成模型。项目页面：[此链接](https://this-url.com/)（请将占位符替换为实际URL）。
URL
https://arxiv.org/abs/2601.00204
