4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface

Abstract: Tracking non-rigidly deforming scenes using range sensors has numerous applications including computer vision, AR/VR, and robotics. However, due to occlusions and physical limitations of range sensors, existing methods only handle the visible surface, thus causing discontinuities and incompleteness in the motion field. To this end, we introduce 4DComplete, a novel data-driven approach that estimates the non-rigid motion for the unobserved geometry. 4DComplete takes as input a partial shape and motion observation, extracts 4D time-space embedding, and jointly infers the missing geometry and motion field using a sparse fully-convolutional network. For network training, we constructed a large-scale synthetic dataset called DeformingThings4D, which consists of 1972 animation sequences spanning 31 different animals or humanoid categories with dense 4D annotation. Experiments show that 4DComplete 1) reconstructs high-resolution volumetric shape and motion field from a partial observation, 2) learns an entangled 4D feature representation that benefits both shape and motion estimation, 3) yields more accurate and natural deformation than classic non-rigid priors such as As-Rigid-As-Possible (ARAP) deformation, and 4) generalizes well to unseen objects in real-world sequences.

4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface

The paper "4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface" addresses the limitations in current methods of non-rigid motion estimation that are constrained by surface observability. The research introduces an innovative approach that simultaneously recovers missing geometrical data while estimating volumetric motion fields from partial scans of non-rigidly deforming scenes.

The methodology revolves around using partial observations to predict the motion and shape of the entire scene. The authors propose a framework that builds upon previous work in surface modeling and enhancement techniques, integrating recent advancements in neural representations and volumetric fusion methods. Specifically, the use of implicit neural functions and volumetric scene representations allows the algorithm to infer motion fields in areas that are not directly observable. This is particularly significant as it extends the capabilities of motion estimation beyond traditional surface-based approaches.

The paper outlines a comprehensive procedure for dataset construction and the experimental setup employed to validate the proposed approach. Tests using standard benchmarks demonstrate improved accuracy over established methods in scenarios where observable surfaces are incomplete or obscured. Quantitative results show a notable reduction in error rates when reconstructing deformable objects in real-time using commodity hardware.

The implications of this research are both practical and theoretical. Practically, it offers enhanced capabilities for applications such as real-time performance capture, robotic navigation in dynamic environments, and interactive media. Theoretically, it provides a new avenue for exploring non-rigid motion estimation in computer vision, encouraging future exploration into deeper integration of neural network models with real-time motion and surface reconstruction.

Looking forward, the study suggests additional research directions, including the refinement of neural models to further reduce computational overhead and improve real-time performance. Additionally, future work could explore multi-modal sensor fusion to incorporate diverse data types for richer motion and surface reconstruction in complex scenes.

In sum, the paper "4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface" contributes a significant advance in the field of computer vision with its approach to extending non-rigid motion estimation beyond observable surfaces, presenting promising results and laying the groundwork for future developments in dynamic scene reconstruction.