ReMatching Dynamic Reconstruction Flow

ICLR 2025 Conference Submission655 Authors

Keywords: Dynamic Reconstruction, Flow Modeling

TL;DR: Introduce the ReMatching framework that enhances dynamic scene reconstruction by integrating velocity-field priors

Abstract:

Reconstructing dynamic scenes from image inputs is a fundamental computer
vision task with many downstream applications. Despite recent advancements, existing
approaches still struggle to achieve high-quality reconstructions from unseen
viewpoints and timestamps. This work introduces the ReMatching framework,
designed to improve generalization quality by incorporating deformation priors into
dynamic reconstruction models. Our approach advocates for velocity-field-based
priors, for which we suggest a matching procedure that can seamlessly supplement
existing dynamic reconstruction pipelines. The framework is highly adaptable
and can be applied to various dynamic representations. Moreover, it supports
integrating multiple types of model priors and enables combining simpler ones to
create more complex classes. Our evaluations on popular benchmarks involving
both synthetic and real-world dynamic scenes demonstrate a clear improvement in
reconstruction accuracy of current state-of-the-art models.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 655