Articulated Motion Representation for Consistent Unsupervised Animation
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Solution Overview
Problem
Existing animation methods struggle to effectively animate articulated objects with distinct parts in an unsupervised manner, failing to decouple foreground from background and maintain object shape and pose consistency.
Innovation Solution
An animation system that identifies object regions, tracks their motions using principal axes, and models non-object related global motion with affine transformations, disentangling shape and pose to facilitate consistent animation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing animation methods are used to animate articulated objects, then animation can be generated, but the methods fail to maintain shape and pose consistency and cannot effectively decouple foreground from background
Solution Approach 1:
The patent segments the articulated object into multiple distinct parts (e.g., head, body, limbs) and processes each part separately through region prediction heatmaps. This segmentation enables independent motion tracking for each body part while maintaining overall coherence, resolving the contradiction by preserving shape consistency through dedicated region representations.
Solution Approach 2:
The patent extracts and separates foreground object regions from the background using region prediction heatmaps and affinity transformations. By isolating the foreground object components and processing them independently from background motion, the method achieves effective foreground-background decoupling while maintaining object integrity.
2Ease of manufacture
If unsupervised animation is applied to articulated objects, then animation can be generated without annotations, but the distinct parts of the object cannot be properly tracked and animated
Solution Approach 1:
The system performs self-service by automatically predicting region heatmaps and motion parameters without requiring external annotations or supervision. The neural network learns to identify and track object parts autonomously from unannotated video data, achieving unsupervised animation while maintaining tracking precision through learned features.
Solution Approach 2:
The patent transforms the tracking problem into parameter estimation by predicting heatmaps and motion parameters (translation, rotation, scale) for each object part. This parameter-based approach enables precise tracking of distinct parts in an unsupervised manner, converting qualitative visual information into quantitative motion descriptors.
3Adaptability or versatility
If global motion is modeled without considering object-specific motion, then background motion can be captured, but object shape and pose consistency is lost
Solution Approach 1:
The patent applies local quality by using affine transformations with different parameters for different object regions rather than a single global transformation. Each body part can have its own motion characteristics while still being part of the overall object motion model, allowing simultaneous capture of global scene motion and local object-specific motion.
Solution Approach 2:
By segmenting the object into distinct parts and modeling motion for each part separately, the system can simultaneously capture global motion trends and local motion variations. This segmentation enables the framework to handle both background motion and object-specific articulation without compromising consistency.
Data Source
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AI summary
Systems and methods herein describe novel motion representations for animating articulated objects consisting of distinct parts. The described systems and method access source image data, identify driving image data to modify image feature data in the source image sequence data, generate, using an image transformation neural network, modified source image data comprising a plurality of modified source images depicting modified versions of the image feature data, the image transformation neural network being trained to identify, for each image in the source image data, a driving image from the driving image data, the identified driving image being implemented by the image transformation neural network to modify a corresponding source image in the source image data using motion estimation differences between the identified driving image and the corresponding source image, and stores the modified source image data.