Fast-Vid2Vid++: Spatial-Temporal Distillation for Real-Time Video-to-Video Synthesis.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Deep Learning

Background:

• Video-to-Video synthesis (Vid2Vid) generates realistic videos from semantic maps but suffers from high computational costs and latency.
• Existing methods primarily optimize network architectures, neglecting the impact of sequential data streams on performance.
• Image-based compression techniques are insufficient for video tasks due to the lack of temporal coherence.

Purpose of the Study:

• To introduce Fast-Vid2Vid++, a spatial-temporal hybrid distillation compression framework for efficient Vid2Vid.
• To address computational cost and inference latency by focusing on knowledge distillation and data stream compression in both space and time.
• To enable real-time, high-resolution video synthesis with reduced computational resources.

Main Methods:

• Implemented a spatial-temporal hybrid knowledge distillation (Spatial-Temporal-HKD) framework.
• Compressed the data stream spatially and reduced temporal redundancy.
• Distilled hierarchical features and temporal coherence knowledge from a teacher to a student network.
• Utilized motion compensation and motion-aware inference (MAI) for frame interpolation and generation.

Main Results:

• Achieved real-time performance of 30-59 FPS on standard benchmarks.
• Reduced computational cost by 28-35× on a single V100 GPU.
• Successfully synthesized high-resolution key-frames using a low-resolution data stream.

Conclusions:

• Fast-Vid2Vid++ effectively reduces computational cost and inference latency in Vid2Vid tasks.
• The spatial-temporal distillation approach enhances efficiency without sacrificing video quality.
• This framework offers a practical solution for real-time, resource-constrained video synthesis applications.