Neural Motion Image Generation With Controllable Latent Codes
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Solution Overview
Problem
Existing image and video generation technologies are limited in representing realistic motion for various objects, often constrained to specific movements and objects, lacking flexibility and requiring manual editing.
Innovation Solution
Utilizing neural networks to generate image and video data with controllable motion by encoding position and motion information into latent codes, employing generative models like GANs and StyleGAN, and applying PCA for alias-free generation, enabling user-controlled manipulation of video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reference video or segmentation is used to indicate object movement, then the synthesized video can be generated, but the motion is limited to specific types and objects
Solution Approach 1:
The patent applies universality by training a single neural network model to generate motion for various types of objects and movements rather than requiring separate models for each object type. The model learns general motion patterns from diverse training data, enabling it to synthesize videos of different objects (people, animals, vehicles) performing various actions (walking, running, jumping) without manual reconfiguration, thus achieving multi-functionality that resolves the limitation to specific objects and movements
2Manufacturing precision
If neural networks are used to generate realistic motion, then high-quality video content can be produced, but computational resources and training time are consumed
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model offline using extensive training data and computational resources before actual video generation is needed. The model learns motion patterns and generates synthetic videos during this preliminary training phase, so that when real-time or near-real-time generation is required, the pre-trained model can quickly produce results without requiring lengthy training processes during deployment, thus reducing the time loss during actual video generation tasks
3Ease of operation
If manual editing is used to control video content, then specific movements can be achieved, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent applies self-service by enabling users to control video generation through simple high-level inputs such as text descriptions or parameter adjustments rather than requiring complex manual frame-by-frame editing. The neural network model automatically interprets these simple inputs and generates the appropriate motion sequences, performing the complex video manipulation tasks autonomously without requiring users to manually edit each frame, thus significantly reducing editing time and labor intensity while maintaining ease of operation
Data Source
AI summary
Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects based, at least in part, on input indicating motion of the one or more objects.