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VideoMaMa from KAIST: A mask-to-matte model, converts coarse video segmentation into pixel-accurate alpha mattes; looks quite good. Based on SVD+DINOv3.

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Wildminder

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19,652 görüntüleme • 5 ay önce •via X (Twitter)

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Create compositing mattes in seconds with Aleph 2.0 Mattes let you isolate a subject from its background so you can composite, color or apply effects to just one part of your shot. 1. Upload your video 2. Prompt for a white silhouette on black 3. Review your preview 4. Hit generate 5. Set your matte clip as a luma matte in your editor Clean mattes, ready for compositing. No rotoscoping required.
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Create compositing mattes in seconds with Aleph 2.0 Mattes let you isolate a subject from its background so you can composite, color or apply effects to just one part of your shot. 1. Upload your video 2. Prompt for a white silhouette on black 3. Review your preview 4. Hit generate 5. Set your matte clip as a luma matte in your editor Clean mattes, ready for compositing. No rotoscoping required.

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Tracking Anything with Decoupled Video Segmentation paper page: Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation.
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Tracking Anything with Decoupled Video Segmentation paper page: Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation.

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Segment Anything is fun and powerful but still requires full mask annotation for supervised training. Our new work MaskFreeVIS appearing at #CVPR2023 shows we can get accurate video instance segmentation WITHOUT any video or image mask annotations. More at
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Segment Anything is fun and powerful but still requires full mask annotation for supervised training. Our new work MaskFreeVIS appearing at #CVPR2023 shows we can get accurate video instance segmentation WITHOUT any video or image mask annotations. More at

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The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️
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The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️

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Tripo Segmentation now breaks 3D models into meaningful parts based on their geometry. Get cleaner boundaries, more accurate part separation, and fewer manual fixes when you need production-ready results. Choose from Simple, Balanced, or Detailed to match your workflow. Perfect for 3D printing, game assets, and downstream editing.
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Tripo Segmentation now breaks 3D models into meaningful parts based on their geometry. Get cleaner boundaries, more accurate part separation, and fewer manual fixes when you need production-ready results. Choose from Simple, Balanced, or Detailed to match your workflow. Perfect for 3D printing, game assets, and downstream editing.

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SAM 2 from Meta FAIR is the first unified model for real-time, promptable object segmentation in images & videos. Using the model in our web-based demo you can segment, track and apply effects to objects in video in just a few clicks. Try SAM 2 ➡️
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SAM 2 from Meta FAIR is the first unified model for real-time, promptable object segmentation in images & videos. Using the model in our web-based demo you can segment, track and apply effects to objects in video in just a few clicks. Try SAM 2 ➡️

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SUPIR upscaler is outdated. ASASR- turns blurry, low-quality photos into sharp, high-res images. Prevents the fake hallucinated details. - improves OCR - high segmentation accuracy - based on FLUX.1 dev this looks sweet
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SUPIR upscaler is outdated. ASASR- turns blurry, low-quality photos into sharp, high-res images. Prevents the fake hallucinated details. - improves OCR - high segmentation accuracy - based on FLUX.1 dev this looks sweet

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Following a lengthy and accurate approach into the second green, Zach Johnson converts the eagle putt. #themasters

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Mattes and Utilities Demo: VFX artist Doug Hogan breaks down a ComfyUI workflow that generates production-ready utility passes directly from your footage. No manual rotoscoping. No guesswork. What's inside: → Prompt-based background removal (RMBG) for fast, clean shots → SAM3 text-prompt segmentation for moving subjects → SAM3 point-based editor for precise matte control → Face segmentation for beauty and skin work in comp → Stable depth and normals passes for relighting and CG integration → App Mode - a simplified UI that hides the node graph entirely Everything ties back to your source plate's native resolution and frame rate automatically via get/set nodes. This is the kind of workflow that used to take a team. Now it runs in ComfyUI. Click the link below for the workflow 👇

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Say goodbye to green screens! DiffusionMat is an AI framework that uses a diffusion model to create refined alpha mattes by correcting the output at every denoising step to look more like the original input image.
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Say goodbye to green screens! DiffusionMat is an AI framework that uses a diffusion model to create refined alpha mattes by correcting the output at every denoising step to look more like the original input image.

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