Neural Spline Deformation Training for Smooth Temporal Motion
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Solution Overview
Problem
Conventional neural deformation models struggle to learn smooth, coherent, and physically plausible deformations across frames and time steps, leading to geometric distortions and artifacts, and are limited in their ability to generalize to complex motions and novel scenarios.
Innovation Solution
A neural deformation model using a spline-based representation that interpolates attributes of a time-varying scene, incorporating a loss function to minimize velocity divergence and acceleration magnitude, allowing for smooth and spatially coherent motion modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If inductive biases (priors, constraints) are introduced in the design and training of a neural deformation model, then geometric distortions and artifacts are reduced, but the flexibility and ability to generalize to novel scenarios is limited
Solution Approach 1:
The patent applies dynamics by making the deformation model adaptive through spline-based representations that can dynamically adjust to different motion types. The model transitions from static inductive biases to dynamic, data-driven representations that adapt to complex motions including fluids, fabrics, and volumetric media, resolving the contradiction between geometric accuracy and generalization ability.
Solution Approach 2:
The patent changes parameters by replacing fixed inductive biases with learnable spline parameters. The neural network learns optimal spline control points and coefficients during training, allowing the model to capture complex deformation patterns while maintaining geometric coherence. This parameter transformation enables the model to generalize to novel scenarios without sacrificing geometric accuracy.
2Stability of the object's composition
If a coordinate neural network is designed to learn smooth and coherent deformations across frames, then geometric distortions are reduced, but the model struggles to generalize to complex motions such as fluids, fabrics, and volumetric media
Solution Approach 1:
The patent applies segmentation by decomposing the deformation field into spline-based segments that can independently represent different motion patterns. The temporal trajectory is divided into controllable segments with learnable control points, allowing the model to maintain temporal coherence within segments while adapting to complex motions across the entire trajectory through the piecewise spline representation.
3Reliability
If conventional neural deformation models use fixed assumptions about local deformations being near-rigid, then physically plausible motion is learned for articulated objects, but the model fails to adapt to complex motions of fluids, fabrics, and volumetric media
Solution Approach 1:
The patent applies universality by creating a unified spline-based deformation model that can handle multiple motion types including rigid articulated objects, fluids, fabrics, and volumetric media. The neural network learns a universal representation that captures the underlying deformation patterns across different material types, replacing the need for separate models or fixed assumptions about local deformation behavior.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for generating a neural deformation model. The technique includes inputting, into a machine learning model, (i) a set of canonical coordinates in a scene and (ii) one or more times included in a temporal trajectory of the scene. The technique also includes generating, via execution of the machine learning model, one or more sets of attributes associated with the set of canonical coordinates and the one or more times. The technique further includes computing one or more losses based on (i) a velocity included in the one or more sets of attributes and (ii) one or more representations of the scene at the one or more times, and training the machine learning model based on the one or more losses.