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cs.CV2026

LCG: Long-Context Consistent Image Generation with Sparse Relational Attention

Zihao Wang, Yijia Xu, Haoze Zheng +3

Recent image generation models achieve impressive quality in single-image synthesis, but often fail to maintain consistency across sequential outputs, as required in comics, storyb…

cs.CV2026

D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models

Dengyang Jiang, Xin Jin, Dongyang Liu +9

The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterparts (e.g, Z-Image-Turbo and…

cs.CV2026

RainFusion2.0: Temporal-Spatial Awareness and Hardware-Efficient Block-wise Sparse Attention

Aiyue Chen, Yaofu Liu, Junjian Huang +6

In video and image generation tasks, Diffusion Transformer (DiT) models incur extremely high computational costs due to attention mechanisms, which limits their practical applicati…

cs.CV2025

VFX Creator: Animated Visual Effect Generation with Controllable Diffusion Transformer

Xinyu Liu, Ailing Zeng, Wei Xue +4

Crafting magic and illusions is one of the most thrilling aspects of filmmaking, with visual effects (VFX) serving as the powerhouse behind unforgettable cinematic experiences. Whi…

cs.CV2024

ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation

Weiming Ren, Huan Yang, Ge Zhang +4

Image-to-video (I2V) generation aims to use the initial frame (alongside a text prompt) to create a video sequence. A grand challenge in I2V generation is to maintain visual consis…