Advancements in Open-Source Video Models

🎥 Lights, Camera, Segmentation! SAM-PT: Unraveling the Video Segmentation! 📹🎩 Lately, I was working on a video segmentation project but was struggling with inconsistent video segmentation performance on unseen data with traditional open-source libraries like OpenCV and Mask R-CNN. Recently I came across SAM-PT, which extends the powerful SAM model to track and segment anything in dynamic videos. 📹💡 No need for extensive training data - SAM-PT achieves impressive zero-shot results by leveraging robust point tracking with state-of-the-art point trackers like PIPS and sparse point selection techniques. It intelligently prompts SAM with these points to produce per-frame segmentation masks, allowing for precise tracking of diverse objects in various video environments. 🎯💡 Check out the SAM-PT code on GitHub: https://buff.ly/3pYSxY6 Link to the paper: https://buff.ly/3K7oAfh #datascience #ai #computervision #videosegmentation

🎥 The world of video editing is witnessing a remarkable advancement with the introduction of Video Instance Matting (VIM). This innovative approach by a team of researchers takes video matting to a new level, allowing for precise isolation of individual instances within a video frame. It's a game-changer for video editing, special effects, and any application that requires detailed manipulation of video content. Abstract: "Conventional video matting outputs one alpha matte for all instances appearing in a video frame so that individual instances are not distinguished. While video instance segmentation provides time-consistent instance masks, results are unsatisfactory for matting applications, especially due to applied binarization. To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence. To tackle this challenging problem, we present MSG-VIM, a Mask Sequence Guided Video Instance Matting neural network, as a novel baseline model for VIM. MSG-VIM leverages a mixture of mask augmentations to make predictions robust to inaccurate and inconsistent mask guidance. It incorporates temporal mask and temporal feature guidance to improve the temporal consistency of alpha matte predictions. Furthermore, we build a new benchmark for VIM, called VIM50, which comprises 50 video clips with multiple human instances as foreground objects. To evaluate performances on the VIM task, we introduce a suitable metric called Video Instance-aware Matting Quality~(VIMQ). Our proposed model MSG-VIM sets a strong baseline on the VIM50 benchmark and outperforms existing methods by a large margin." Credit:  SHI Labs, Georgia Institute of Technology, University of Oregon, University of Illinois Urbana-Champaign, Picsart AI Research (PAIR) Hugging Face Page: https://lnkd.in/eXz38smC arXiv: https://lnkd.in/eHBdYsib GitHub: https://lnkd.in/eARaedBd MIT License: https://lnkd.in/efsV6TAR For more like this ⤵ 👉 Follow Orbis Tabula #segementation #neuralnetworks #AIcompositing

A breakthrough in video super-resolution. Upscale-A-Video is a Temporal-Consistent Diffusion Model that leverages text prompts to upscale low-resolution videos. Overcoming challenges of fidelity and temporal consistency, their model integrates temporal layers, a recurrent latent propagation module, and a fine-tuned VAE-Decoder for exceptional results. You can enjoy flexibility with adjustable noise levels and text-guided texture creation, striking the perfect balance between restoration and generation. Extensive experiments demonstrate superior performance in both synthetic and real-world benchmarks, showcasing impressive visual realism and temporal consistency. Check out more details about the project: https://lnkd.in/dmmnG46m Research paper: https://lnkd.in/dPJCf277 #AI #Video #Innovation