We present xGen-MM-Vid (BLIP-3-Video): a multimodal language model for videos,
particularly designed to efficiently capture temporal information over multiple frames. BLIP-3 …

In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the
post-training phase by distilling a highly capable consistency model from a pretrained T2V …

Current frontier video diffusion models have demonstrated remarkable results at generating
high-quality videos. However, they can only generate short video clips, normally around 10 …

Contrastive Language-Image Pre-training (CLIP) has been widely studied and applied in
numerous applications. However, the emphasis on brief summary texts during pre-training …

Real-world videos consist of sequences of events. Generating such sequences with precise
temporal control is infeasible with existing video generators that rely on a single paragraph …

As visual generation technologies continue to advance, the scale of video datasets has
expanded rapidly, and the quality of these datasets is critical to the performance of video …

Large Vision-Language Models (VLMs) have been extended to understand both images
and videos. Visual token compression is leveraged to reduce the considerable token length …

Despite advances in Large Multi-modal Models, applying them to long and untrimmed video
content remains challenging due to limitations in context length and substantial memory …

This is the technique report for the winning solution of the CVPR2024 GenAI Media
Generation Challenge Workshop's Instruction-guided Image Editing track. Instruction-guided …

Long-context modeling is a critical capability for multimodal large language models
(MLLMs), enabling them to process long-form contents with implicit memorization. Despite …