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cs.CV2026

MoAKE: Toward Unified All-in-One Action Quality Assessment via Mixture of Action Knowledge Experts

Huangbiao Xu, Huanqi Wu, Xiao Ke +3

Action Quality Assessment (AQA) aims to objectively evaluate performance quality from action videos. Most existing methods follow a ``one-by-one'' paradigm, training a separate mod…

cs.CV2026

Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL

Junyi Wu, Weijian Luo, Haoyang Zheng +2

Recent advances in one-step text-to-image generation have enabled real-time synthesis with remarkable efficiency and quality. Previous reinforcement learning methods for one-step g…

cs.CV2026

FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing

Junyi Wu, Zhiteng Li, Haotong Qin +2

Text-guided image editing with diffusion models has achieved remarkable quality but often suffers from prohibitive latency. We introduce \textbf{FlashEdit}, a real-time localized i…

cs.CV2026

DVD-Quant: Data-free Video Diffusion Transformers Quantization

Zhiteng Li, Hanxuan Li, Junyi Wu +6

Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While…

cs.CV2026

AdaTSQ: Pushing the Pareto Frontier of Diffusion Transformers via Temporal-Sensitivity Quantization

Shaoqiu Zhang, Zizhong Ding, Kaicheng Yang +6

Diffusion Transformers (DiTs) have emerged as the state-of-the-art backbone for high-fidelity image and video generation. However, their massive computational cost and memory footp…

cs.CV2025

MCMoE: Completing Missing Modalities with Mixture of Experts for Incomplete Multimodal Action Quality Assessment

Huangbiao Xu, Huanqi Wu, Xiao Ke +3

Multimodal Action Quality Assessment (AQA) has recently emerged as a promising paradigm. By leveraging complementary information across shared contextual cues, it enhances the disc…