SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes (2308.08258v1)

Abstract: Existing methods for the 4D reconstruction of general, non-rigidly deforming objects focus on novel-view synthesis and neglect correspondences. However, time consistency enables advanced downstream tasks like 3D editing, motion analysis, or virtual-asset creation. We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner. Our dynamic-NeRF method takes multi-view RGB videos and background images from static cameras with known camera parameters as input. It then reconstructs the deformations of an estimated canonical model of the geometry and appearance in an online fashion. Since this canonical model is time-invariant, we obtain correspondences even for long-term, long-range motions. We employ neural scene representations to parametrize the components of our method. Like prior dynamic-NeRF methods, we use a backwards deformation model. We find non-trivial adaptations of this model necessary to handle larger motions: We decompose the deformations into a strongly regularized coarse component and a weakly regularized fine component, where the coarse component also extends the deformation field into the space surrounding the object, which enables tracking over time. We show experimentally that, unlike prior work that only handles small motion, our method enables the reconstruction of studio-scale motions.

• The paper presents a method that achieves time-consistent dynamic scene reconstruction via a time-invariant canonical model and coarse-to-fine deformation fields.
• It employs backward space warping and end-to-end differentiability to maintain robust temporal correspondences and handle large-scale deformations.
• Experimental results reveal superior accuracy in correspondence tracking and 3D editing capabilities compared to existing dynamic-NeRF approaches.

Evaluating SceNeRFlow: Time-Consistent Reconstruction of Dynamic Scenes

The research paper introduces SceNeRFlow, a method for reconstructing general dynamic scenes in a time-consistent manner using a neural radiance field (NeRF) based approach. Existing dynamic-NeRF methodologies typically emphasize novel-view synthesis without preserving temporal correspondences across frames, limiting their utility for post-processing tasks such as 3D editing and motion analysis. In contrast, SceNeRFlow focuses on maintaining consistent correspondences over time by reconstructing a deformation model of a canonical space, showcasing its potential for applications requiring persistent 3D structures.

Key Contributions

SceNeRFlow is developed with a central goal: achieving time-consistency in reconstructing non-rigid, dynamically deforming scenes from multi-view static-camera RGB sequences. The method introduces significant innovations over previous dynamic-NeRF techniques:

• Canonical Model and Deformations: Instead of using a time-variable model for geometry and appearance, SceNeRFlow reconstructs a time-invariant canonical model. Deformations are decomposed into coarse and fine components to enable better tracking and handling of large motions.
• Extended Deformation Field: A novel adaptation involving the extension of deformation fields into surrounding space, which is critical for preserving long-term correspondences needed for large-scale and long-range motion tracking.
• End-to-End Differentiability and Scene Consistency: The entire scene is treated in a differentiable manner, allowing for seamless integration into modern neural pipelines and practical tasks like 3D object editing.

Methodological Advancements

SceNeRFlow utilizes backward space warping combined with coarse and fine deformation components. The proposed decomposition optimizes tracking effectiveness and time consistency by distinctly handling large-scale transformations through coarse regularization and preserving detail with a fine-adaptation. Unique to SceNeRFlow is its ability to reconstruct substantial and complex scene motions with studio-scale actions without category-specific priors, which remains a challenge in similar works.

Experimental Results

Experiments reveal SceNeRFlow's robust capability to handle large deformations and irregular object motions. The paper contrasts its results with existing benchmarks, like NR-NeRF and PREF, demonstrating superior correspondence accuracy across time with minimal drift. Quantitative evaluations report excellent time consistency with significant performance gaps over alternatives in terms of mean per-joint position error (MPJPE). Additionally, SceNeRFlow depicts impressive 3D editing capabilities, reinforcing its potential for graphical applications.

Future Implications and Speculations

SceNeRFlow presents substantial implications for the fields of computer graphics, animation, and augmented reality by enabling advanced editing and interaction with 3D assets that are consistent over time. The approach heralds new opportunities for developing applications necessitating precise dynamic reconstructions, such as virtual reality environments and non-linear video editing. Furthermore, future research could lower the dependency on multi-view setups by incorporating single or few camera systems through improved monocular consistency frameworks.

In sum, SceNeRFlow signifies a progressive step toward achieving high fidelity and time-consistent dynamic scene reconstruction. Its impacts on related domains underscore the potential for evolving AI mechanisms that can handle intricate temporal phenomena with greater accuracy, opening pathways for sophisticated engagement within three-dimensional virtual domains.