MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing
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/.
695b4545832867f253525dc5
Xiaokun Sun
claimed_verified
2026-01-05T08:27:11.914Z
65813fbeabafd960c84fdf2f
https://cdn-avatars.huggingface.co/v1/production/uploads/65813fbeabafd960c84fdf2f/-2YSyWi8M3hZC2V_Qcss5.png
Xiaokun Sun
XiaokunSun
user
XiaokunSun
695b4545832867f253525dc6
Zeyu Cai
695b4545832867f253525dc7
Hao Tang
695b4545832867f253525dc8
Ying Tai
695b4545832867f253525dc9
Jian Yang
695b4545832867f253525dca
Zhenyu Zhang
MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing
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/.