Action Video Generation with Disentangled Motion and Content Control
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Solution Overview
Problem
The extension from generating images to generating videos is a challenging task due to the need to learn plausible physical motion models of objects, handle speed variations, and sensitivity to motion artifacts, which existing deep generative models struggle to address effectively.
Innovation Solution
A method involving a recurrent neural network and a generator neural network decompose the latent space into a content and motion subspace, allowing for controlled video generation by traversing these subspaces to produce videos with varying speeds and lengths, using a generative adversarial network framework for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep generative models are used to generate videos, then image generation capability is improved, but motion plausibility deteriorates
Solution Approach 1:
The patent segments the video generation task into two independent components: a content generation network that produces spatial information and a motion generation network that produces temporal motion vectors. This segmentation allows each network to specialize in its respective domain, ensuring that motion plausibility is maintained while image generation quality is improved through the content network's expertise in spatial representation.
Solution Approach 2:
The patent introduces an intermediary mechanism where motion vectors are generated independently and then combined with content representations through a generator network. This intermediary approach allows the motion and content to be optimized separately before integration, resolving the contradiction between image quality and motion plausibility by ensuring both components are high-quality before they work together.
2Adaptability or versatility
If motion models are learned to capture speed variations, then video diversity is improved, but motion artifacts increase
Solution Approach 1:
The patent employs a recurrent neural network that generates motion vectors dynamically based on temporal patterns in the input video. This dynamic approach allows the model to adapt to varying speeds and motion patterns in real-time, capturing speed variations while avoiding motion artifacts by continuously adjusting motion vectors according to the actual video content rather than using fixed or pre-defined motion models.
Solution Approach 2:
The patent incorporates feedback mechanisms where the generator network receives both content representations and motion vectors, and the motion generation network adjusts its output based on the content context. This feedback loop ensures that speed variations are captured accurately while motion artifacts are minimized by continuously refining motion vectors based on the actual video content and temporal relationships.
3Reliability
If physical motion models are learned, then motion realism is improved, but computational complexity increases
Solution Approach 1:
The patent segments the computational task into separate content generation and motion generation networks, each handling a specific aspect of video synthesis. This segmentation reduces the computational complexity of the overall system by allowing each network to be optimized for its specific function rather than requiring a single complex model to handle both content and motion simultaneously, while still achieving motion realism through specialized motion vector generation.
Data Source
AI summary
A method, computer readable medium, and system are disclosed for action video generation. The method includes the steps of generating, by a recurrent neural network, a sequence of motion vectors from a first set of random variables and receiving, by a generator neural network, the sequence of motion vectors and a content vector sample. The sequence of motion vectors and the content vector sample are sampled by the generator neural network to produce a video clip.