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World Action Models (WAMs) jointly model action generation and environment dynamics and are mostly built on pretrained Video Diffusion Models (VDMs). In VDM-based WAMs, observations are first encoded by a VAE, and the resulting compressed latents are then processed by large video diffusion backbones to extract effectiv...
Large-scale vision-language pretrained models like CLIP, paired with parameter-efficient fine-tuning techniques, have emerged as promising solutions for image-to-video transfer in video action recognition. However, existing methods often prioritize strong supervised performance at the expense of transferability and gen...
This work presents VideoXAgent, a purely online video-agent harness for long video understanding that starts from the given video file and user query, plans and decomposes the task, invokes specialized expert tools on demand, and aggregates multimodal evidence to produce a final answer while resolving conflicts among o...
Multimodal Large Language Models (MLLMs) have been growing the capability for scientific writing and collaboration. For example, OpenAI Prism is a free workspace for scientific writing and collaboration. One important feature in Prism is turning scientific diagrams directly into LaTeX TikZ code. In this paper, we build...
From-Draft-to-Draft-Free (D2DF), a framework that distills the ability of transforming coarse drafts into refined videos into a one-step video generation model, achieves state-of-the-art performance on multiple metrics, surpassing traditional and multi-step generative methods in both quality and efficiency.
GroundShot is presented, a training-free, model-agnostic agentic framework for entity-grounded multi-shot generation that improves multi-shot consistency over existing methods while requiring no additional training or model modification.
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