Gaussian Splatting with Neural Spline Deformation for Smooth Scene Motion
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Solution Overview
Problem
Conventional neural deformation models struggle to learn smooth, coherent, and physically plausible deformations across frames, leading to geometric distortions and artifacts, and are limited in generalizing to complex motions and novel scenarios.
Innovation Solution
A spline-based representation is used to model time-varying deformations, interpolating attributes in a smooth and spatially coherent manner, using a machine learning model to generate deformed attributes via spline interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If inductive biases (priors, constraints) are introduced in neural deformation models to mitigate geometric distortions and artifacts, then geometric accuracy is improved, but the flexibility and ability to generalize to novel scenarios deteriorates
Solution Approach 1:
The patent applies dynamics by making the deformation model adaptive rather than static. The neural network learns to dynamically adjust deformation patterns based on input data, allowing the model to generalize to novel scenarios without being constrained by fixed inductive biases. This enables the system to maintain geometric accuracy while adapting to diverse deformation types including fluids, fabrics, and volumetric media.
Solution Approach 2:
The patent changes the parameters of the deformation model by using a neural network with learnable parameters instead of fixed inductive biases. The model learns optimal deformation parameters from data, allowing it to adapt to different scenarios (rigid objects, fluids, fabrics) by adjusting its internal parameters rather than being constrained by pre-defined assumptions.
2Device complexity
If conventional neural deformation models are used to learn time-varying deformations, then the model structure is simple, but the ability to learn smooth and coherent deformations across frames deteriorates
Solution Approach 1:
The patent introduces an intermediary mechanism in the form of a neural network that mediates between simple model structure and smooth deformation learning. The network acts as an intermediary layer that processes temporal information and spatial coordinates to produce coherent deformation fields, bridging the gap between model simplicity and deformation quality.
Solution Approach 2:
The patent adds temporal dimension processing to the deformation model by incorporating time-varying inputs and producing deformations across multiple frames. This dimensional extension allows the model to learn temporal coherence while maintaining spatial accuracy, achieving smooth deformations across time without significantly increasing structural complexity.
3Reliability
If inductive biases assuming near-rigid local deformations are used, then physically plausible motion in articulated objects is achieved, but the ability to generalize to complex motions in fluids and fabrics deteriorates
Solution Approach 1:
The patent applies universality by creating a single deformation model that can handle multiple types of motions and materials. The neural network is designed to be multi-functional, capable of processing rigid object deformations, fluid motions, fabric draping, and volumetric media transformations within a unified framework, eliminating the need for separate models for different motion types.
Solution Approach 2:
The patent makes the deformation model dynamic by allowing it to adapt its behavior based on the input characteristics. The neural network learns to dynamically adjust its deformation patterns to match the physical properties of different materials and motion types, maintaining physical plausibility across diverse scenarios without being constrained by fixed assumptions about rigidity.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for determining a time-varying deformation associated with a scene. The technique includes matching a query time to a time interval associated with the scene and generating, via execution of a machine learning model, (i) a first set of attributes associated with a set of canonical coordinates in the scene at a starting time of the time interval and (ii) a second set of attributes associated with the set of canonical coordinates at an ending time of the time interval. The technique also includes computing a third set of attributes associated with the set of canonical coordinates at the query time based on a spline interpolation associated with the first and second sets of attributes. The technique further includes generating a representation of the scene at the query time based on the third set of attributes.