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GRNEdit achieves superior video editing performance with significantly fewer parameters, challenging the notion that bigger models are always better.
Reflection removal in videos captured through glass can now be achieved in a single step, outperforming traditional methods with state-of-the-art results and faster inference.
Achieving state-of-the-art identity fidelity and temporal stability, TIGER redefines the standards for high-quality face video restoration.
RS-Gen achieves state-of-the-art performance in image generation by autonomously identifying and resolving logical issues and knowledge gaps in real-time.
STAR Allocation enables targeted policy updates in text-to-image generation, leading to unprecedented improvements in semantic alignment and text rendering.
Generate native 2K/4K videos 10x faster without sacrificing visual quality by decoupling global structure modeling from fine-grained detail synthesis.
AutoAWG cuts FID and FVD by up to 50% while generating high-fidelity adverse weather videos, revolutionizing data generation for autonomous driving.
