TL;DR
MotionScale is a scalable 4D Gaussian Splatting framework that reconstructs accurate, temporally consistent dynamic scenes from monocular videos, effectively handling large scenes and extended sequences.
Contribution
It introduces a novel scalable motion field with cluster-centric basis transformations and a progressive optimization strategy for robust long-term dynamic scene reconstruction.
Findings
Outperforms state-of-the-art methods in reconstruction quality.
Achieves high temporal stability in dynamic scene reconstruction.
Effectively models complex and evolving motion patterns.
Abstract
Realistic reconstruction of dynamic 4D scenes from monocular videos is essential for understanding the physical world. Despite recent progress in neural rendering, existing methods often struggle to recover accurate 3D geometry and temporally consistent motion in complex environments. To address these challenges, we propose MotionScale, a 4D Gaussian Splatting framework that scales efficiently to large scenes and extended sequences while maintaining high-fidelity structural and motion coherence. At the core of our approach is a scalable motion field parameterized by cluster-centric basis transformations that adaptively expand to capture diverse and evolving motion patterns. To ensure robust reconstruction over long durations, we introduce a progressive optimization strategy comprising two decoupled propagation stages: 1) A background extension stage that adapts to newly visible regions,…
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