Point Cloud Animation Using Bone Data for Editable Motion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for animating point cloud data or Gaussian splat data are limited, as they typically lack data on the relationship between points, making it difficult to create or adjust motions of objects beyond what is captured in real-world imaging, which is a challenge in fields like video game and animated film development.
Innovation Solution
A method is provided to animate point cloud data by identifying moving parts of an object from moving image data, generating bone data linked to sets of points, and adjusting bone parameters iteratively to match desired motions, using differentiable rasterization and gradient descent optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate and independent Gaussian splat datasets are generated for each frame of captured image data, then the object motion can be depicted, but the motion is limited to what was initially imaged and cannot be freely created or adjusted
Solution Approach 1:
The patent introduces an intermediary representation layer between the point cloud data and the final animation output. This intermediary system uses bone data structures that act as mediators, linking hierarchical relationships between points and enabling controlled transformation. The bone data serves as an intermediate abstraction that allows motion parameters to be defined independently from the raw point cloud, thus providing motion flexibility without requiring complete re-generation of Gaussian splat datasets for each desired motion.
2Ease of operation
If bone data structures are introduced to link points hierarchically, then motion control flexibility is improved, but the data processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the point cloud data into hierarchical groups organized through bone data structures. Instead of treating all points uniformly, the system segments points into parent-child relationships where parent points represent broader object regions and child points represent finer details. This segmentation enables localized motion control - adjusting a parent bone automatically transforms all associated child points, making motion adjustment easier while managing complexity through hierarchical organization.
Solution Approach 2:
The bone data structures are designed to be dynamic rather than static, allowing hierarchical relationships and transformation parameters to be modified during runtime. The system enables dynamic adjustment of bone transformations, point associations, and hierarchical structures, providing flexibility in motion creation and adjustment without requiring complete system reconfiguration.
3Manufacturing precision
If iterative optimization with differentiable rasterization is used to match bone parameters to target motions, then animation accuracy is improved, but computational time increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing differentiable rasterization capabilities within the bone data structures. Instead of performing complex optimization calculations from scratch for each animation scenario, the system prepares transformation matrices, point associations, and rasterization derivatives in advance. This preliminary preparation enables faster iterative optimization during actual animation creation, as the computational groundwork has already been laid, reducing the time penalty of achieving high motion matching accuracy.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A computer-implemented method of animating point cloud data of a scene, comprising the steps of: obtaining the point cloud data of the scene, wherein the scene comprises an object; obtaining moving image data of the scene, the moving image data depicting movement of the object within the scene; identifying, using the moving image data, moving parts of the object; identifying, from the point cloud data, sets of points corresponding to the moving parts of the object; and generating bone data associated with the point cloud data, wherein the bone data defines bones that are each linked to a respective set of points such that, when in use, movement of the bones causes movement of the sets of points, thereby animating the point cloud data.