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cs.CV2026

When Physical Preferences Meet Semantic Constraints: Physical and Semantic Direct Preference Optimization for Text-to-Video Generation

Siwei Meng, Yawei Luo, Shu Zhang +1

Text-to-video (T2V) generation models have achieved strong visual realism, but improving physical plausibility can come at the cost of semantic consistency with the input text. Thi…

cs.CV2026

Referring Layer Decomposition

Fangyi Chen, Yaojie Shen, Lu Xu +4

Precise, object-aware control over visual content is essential for advanced image editing and compositional generation. Yet, most existing approaches operate on entire images holis…

cs.CV2025

EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation

Bingxuan Li, Yiming Cui, Yicheng He +4

Sound effects build an essential layer of multimodal storytelling, shaping the emotional atmosphere and the narrative semantics of videos. Despite recent advancement in video-text-…

cs.CV2025

Vidi2.5: Large Multimodal Models for Video Understanding and Creation

Vidi Team, Chia-Wen Kuo, Chuang Huang +31

Video has emerged as the primary medium for communication and creativity on the Internet, driving strong demand for scalable, high-quality video production. Vidi models continue to…

cs.CV20241 cited

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models

Le Xue, Manli Shu, Anas Awadalla +30

This paper introduces BLIP-3, an open framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model archi…