MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics
Summary
MoCam is a research paper introducing a diffusion-based framework for unified novel view synthesis that dynamically coordinates geometric and appearance priors to improve robustness against geometric errors.
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Paper page - MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics
Source: https://huggingface.co/papers/2605.12119
Abstract
MoCam addresses the challenge of generative novel view synthesis by dynamically coordinating geometric and appearance priors through structured denoising dynamics within a diffusion framework.
Generative novelview synthesisfaces a fundamental dilemma:geometric priorsprovide spatial alignment but become sparse and inaccurate under view changes, whileappearance priorsoffer visual fidelity but lack geometric correspondence. Existing methods either propagategeometric errorsthroughout generation or suffer from signal conflicts when fusing both statically. We introduce MoCam, which employs structureddenoising dynamicsto orchestrate a coordinated progression from geometry to appearance within thediffusion process.MoCam first leveragesgeometric priorsin early stages to anchor coarse structures and tolerate their incompleteness, then switches toappearance priorsin later stages to actively correctgeometric errorsand refine details. This design naturally unifies static and dynamicview synthesisby temporally decoupling geometric alignment andappearance refinementwithin thediffusion process.Experiments demonstrate that MoCam significantly outperforms prior methods, particularly whenpoint cloudscontain severe holes or distortions, achieving robust geometry-appearance disentanglement.
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