MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics

TL;DR

MoCam introduces a diffusion-based method that sequentially integrates geometric and appearance priors for improved novel view synthesis, especially in challenging scenarios with incomplete geometry.

Contribution

It proposes a structured denoising dynamic approach that unifies static and dynamic view synthesis by decoupling geometry and appearance refinement over diffusion stages.

Findings

MoCam outperforms prior methods on challenging datasets with geometric holes.

It achieves robust geometry-appearance disentanglement.

The method effectively refines details by switching from geometry to appearance priors.

Abstract

Generative novel view synthesis faces a fundamental dilemma: geometric priors provide spatial alignment but become sparse and inaccurate under view changes, while appearance priors offer visual fidelity but lack geometric correspondence. Existing methods either propagate geometric errors throughout generation or suffer from signal conflicts when fusing both statically. We introduce MoCam, which employs structured denoising dynamics to orchestrate a coordinated progression from geometry to appearance within the diffusion process. MoCam first leverages geometric priors in early stages to anchor coarse structures and tolerate their incompleteness, then switches to appearance priors in later stages to actively correct geometric errors and refine details. This design naturally unifies static and dynamic view synthesis by temporally decoupling geometric alignment and appearance refinement within the diffusion process. Experiments demonstrate that MoCam significantly outperforms prior methods, particularly when point clouds contain severe holes or distortions, achieving robust geometry-appearance disentanglement.