Point Cloud Rigging with Bone Data for Editable 3D Motion
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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 those captured in real-world imaging.
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
1Illumination intensity
If point cloud data is used to represent 3D scenes, then rendering flexibility and visual quality are improved, but the ability to animate and control object motions is worsened due to lack of relational data between points
Solution Approach 1:
The patent introduces bone data as an intermediary structure that mediates between the point cloud representation and animation control. The bone data defines hierarchical relationships and transformations that enable motion control without modifying the underlying point cloud structure, thus preserving visual quality while adding animation capability.
Solution Approach 2:
The patent segments the point cloud data into multiple objects, each with its own bone data structure. This segmentation allows independent animation control of different objects within the scene while maintaining the overall visual fidelity of the point cloud representation.
2Measurement precision
If separate Gaussian splat datasets are generated for each frame of captured image data, then motion capture accuracy is improved, but device complexity and processing requirements are worsened
Solution Approach 1:
The patent performs preliminary action by generating Gaussian splat data from a single reference frame and then reusing this data across multiple frames. Instead of regenerating splat data for each frame, the system adjusts the existing splat positions and transformations based on bone data, significantly reducing processing complexity while maintaining motion accuracy.
Solution Approach 2:
The patent uses copying by replicating the Gaussian splat data structure across frames and adjusting transformations rather than recreating the entire dataset. The bone data serves as a transformation template that is applied to copy and adapt the reference frame splat data to subsequent frames.
3Adaptability or versatility
If bone data structures are added to enable animation control, then animation versatility is improved, but data structure complexity and processing overhead are worsened
Solution Approach 1:
The patent implements dynamics by making the bone data structure adaptable and configurable. The system allows dynamic adjustment of bone hierarchy, transformation parameters, and object associations, enabling flexible animation control while keeping the base data structure relatively simple and efficient.
Data Source
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.