MorphAny3D：释放结构化隐空间在三维形变中的潜能

MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing

January 1, 2026

作者: Xiaokun Sun, Zeyu Cai, Hao Tang, Ying Tai, Jian Yang, Zhenyu Zhang

cs.AI

摘要

由于生成语义一致且时序平滑的变形序列存在困难——尤其是在跨类别场景下，3D形变技术仍面临挑战。本文提出MorphAny3D，一种基于结构化潜在表征（SLAT）的无训练框架，可实现高质量3D形变。我们的核心发现是：通过智能融合源目标SLAT特征到3D生成器的注意力机制中，能够自然产生逼真的形变序列。为此，我们设计了形变交叉注意力（MCA）模块——通过融合源目标信息保持结构连贯性，以及时序融合自注意力（TFSA）模块——通过引入前一帧特征增强时序一致性。此外，方向校正策略有效缓解了形变过程中的姿态模糊问题。大量实验表明，本方法能生成最先进的形变序列，即使在挑战性跨类别案例中亦表现出色。MorphAny3D进一步支持解耦形变与3D风格迁移等高级应用，并可推广至其他基于SLAT的生成模型。项目页面：https://xiaokunsun.github.io/MorphAny3D.github.io/。

English

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: https://xiaokunsun.github.io/MorphAny3D.github.io/.