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cs.CV2026
In-Context Forcing: Uncovering Context Effects in Autoregressive Video Diffusion
Lingxiao Yang, Liu Liu, Moran Li +4
Current few-step autoregressive video diffusion models depend on previous fully denoised clean frames as context for all denoising steps of the current frame. However, these clean…
cs.CV2026
SteerVTE: Seamless Video Text Editing with Style and Glyph Control
Kai Zeng, Moran Li, Zhengwei Wang +6
Visual text editing aims to precisely modify text in images and videos while preserving stylistic consistency and visual realism. Despite significant advances in the image domain,…
cs.CV2026
TextSculptor: Training and Benchmarking Scene Text Editing
Yiheng Lin, Siyu Jiao, Xiaohan Lan +12
Recent advances in Multimodal Large Language Models (MLLMs) and diffusion-based generative models have substantially improved prompt-driven image editing. However, scene text editi…