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Finetuning the model in the streaming mode will greatly improve the performance.

We leave it for future work. Please reload this page. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-RB achieves a new state-of-the-art accuracy of %, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters.

You switched accounts on another tab or window. We implement an experimental streaming mode without training. Notifications You must be signed in to change notification settings Fork Star 1. Branches Tags. You signed out in another tab or window.

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GitHub k4yt3x video2x A : We introduce Video-MME, the first-ever full-spectrum, M ulti- M odal E valuation benchmark of MLLMs in Video analysis

Reload to refresh your session. 💡 I also have other video-language projects that may interest you. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the. Go to file. We provide several models of varying scales for robust and consistent video depth estimation.

Video-R1 significantly outperforms previous models across most benchmarks. Download the checkpoints listed here and put them under the checkpoints directory. Notifications You must be signed in to change notification settings.

Uh oh! Please refer to Benchmark. Video-LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ⭐ on GitHub for latest update. This work presents Video Depth Anything based on Depth Anything V2which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability.

You signed in with another tab or window. ByteDance †Corresponding author This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability.

Dismiss alert. We hack our pipeline to align the original inference setting in the offline mode. There was an error while loading. Open-Sora Plan: Open-Source Large Video Generation Model. Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth accuracy.

Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth. Video-Depth-Anything-Small model is under the Apache For business cooperation, please send an email to Hengkai Guo at guohengkaighk gmail.

In details, we save the hidden states of temporal attentions for each frames in the caches, and only send a single frame into our video depth model during inference by reusing these past hidden states in temporal attentions.

Skip to content. Due to the inevitable gap between training and testing, we observe a performance drop between the streaming model and the offline model e.