Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing

TL;DR

Interp3D introduces a training-free framework for smooth, texture-aware 3D morphing that maintains geometric and appearance consistency, advancing 3D content creation and animation.

Contribution

It proposes a novel, training-free method combining generative priors and progressive alignment for textured 3D morphing, addressing limitations of existing shape-only or 2D-based approaches.

Findings

Outperforms previous methods in fidelity and smoothness

Demonstrates robustness across graded difficulty levels

Validated by quantitative metrics and human studies

Abstract

Textured 3D morphing seeks to generate smooth and plausible transitions between two 3D assets, preserving both structural coherence and fine-grained appearance. This ability is crucial not only for advancing 3D generation research but also for practical applications in animation, editing, and digital content creation. Existing approaches either operate directly on geometry, limiting them to shape-only morphing while neglecting textures, or extend 2D interpolation strategies into 3D, which often causes semantic ambiguity, structural misalignment, and texture blurring. These challenges underscore the necessity to jointly preserve geometric consistency, texture alignment, and robustness throughout the transition process. To address this, we propose Interp3D, a novel training-free framework for textured 3D morphing. It harnesses generative priors and adopts a progressive alignment principle to ensure both geometric fidelity and texture coherence. Starting from semantically aligned interpolation in condition space, Interp3D enforces structural consistency via SLAT (Structured Latent)-guided structure interpolation, and finally transfers appearance details through fine-grained texture fusion. For comprehensive evaluations, we construct a dedicated dataset, Interp3DData, with graded difficulty levels and assess generation results from fidelity, transition smoothness, and plausibility. Both quantitative metrics and human studies demonstrate the significant advantages of our proposed approach over previous methods. Source code is available at https://github.com/xiaolul2/Interp3D.