Audio-Supervised Video Domain Translation for Temporal Consistency
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Solution Overview
Problem
Existing methods struggle to generate artificial videos with changed domains while maintaining temporal consistency and semantic content, necessitating manual collection of diverse datasets for training machine-learning algorithms.
Innovation Solution
Utilizing audio data to supervise the generation of output videos by encoding input videos and audio into a latent vector, employing an encoder and generator trained to maintain temporal consistency and domain change, with a discriminator for supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual dataset collection is used to train machine-learning algorithms for different domains, then training data diversity is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent uses a generative adversarial network (GAN) to copy and transform video content from a source domain to a target domain. The generator network creates synthetic videos that replicate the semantic content of original videos while changing domain-specific characteristics such as lighting conditions, weather, or camera parameters, eliminating the need for manual collection of diverse datasets
Solution Approach 2:
The patent transforms videos by changing domain parameters such as lighting conditions, weather patterns, resolution, or color spaces while preserving the underlying semantic content. This allows the same video content to be used for training across multiple domains by simply adjusting transformation parameters rather than collecting new videos for each domain
2Adaptability or versatility
If domain-specific video data is transformed to different domains, then training data versatility is improved, but temporal consistency may be lost
Solution Approach 1:
The patent employs a discriminator network that provides feedback to the generator during training. The discriminator evaluates whether generated videos maintain temporal consistency and semantic fidelity, guiding the generator to produce transformations that preserve temporal relationships while achieving domain changes. This feedback mechanism ensures that temporal consistency is maintained across domain transformations
3Ease of manufacture
If traditional video processing methods are used to change domains, then implementation simplicity is maintained, but manufacturing precision and control over transformation quality deteriorate
Solution Approach 1:
The patent creates a universal GAN framework that can perform multiple domain transformation tasks through a single system. The same generator and discriminator architecture can transform videos across different domains (lighting, weather, resolution, etc.) by simply changing training data and transformation parameters, providing both implementation simplicity and precise control over transformation quality
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive an input video of a scene and audio data associated with the input video, the input video being in a first domain; execute an encoder to map the input video and the audio data to a latent vector in a lower-dimensional latent space; and execute a generator to generate an output video of the scene from the latent vector, the output video being in a second domain. The encoder and the generator are trained to maintain temporal consistency between the input video and the output video by using the audio data.