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We’re introducing Segmentation. SVG masks from prompt, points, or box. SOTA on benchmarks.

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moondream

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153,393 views • 7 months ago •via X (Twitter)

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Segment Any 3D Gaussians paper page: Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation. However, existing methods face challenges in either achieving fine-grained, multi-granularity segmentation or contending with substantial computational overhead, inhibiting real-time interaction. In this paper, we introduce Segment Any 3D GAussians (SAGA), a novel 3D interactive segmentation approach that seamlessly blends a 2D segmentation foundation model with 3D Gaussian Splatting (3DGS), a recent breakthrough of radiance fields. SAGA efficiently embeds multi-granularity 2D segmentation results generated by the segmentation foundation model into 3D Gaussian point features through well-designed contrastive training. Evaluation on existing benchmarks demonstrates that SAGA can achieve competitive performance with state-of-the-art methods. Moreover, SAGA achieves multi-granularity segmentation and accommodates various prompts, including points, scribbles, and 2D masks. Notably, SAGA can finish the 3D segmentation within milliseconds, achieving nearly 1000x acceleration compared to previous SOTA.
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Segment Any 3D Gaussians paper page: Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation. However, existing methods face challenges in either achieving fine-grained, multi-granularity segmentation or contending with substantial computational overhead, inhibiting real-time interaction. In this paper, we introduce Segment Any 3D GAussians (SAGA), a novel 3D interactive segmentation approach that seamlessly blends a 2D segmentation foundation model with 3D Gaussian Splatting (3DGS), a recent breakthrough of radiance fields. SAGA efficiently embeds multi-granularity 2D segmentation results generated by the segmentation foundation model into 3D Gaussian point features through well-designed contrastive training. Evaluation on existing benchmarks demonstrates that SAGA can achieve competitive performance with state-of-the-art methods. Moreover, SAGA achieves multi-granularity segmentation and accommodates various prompts, including points, scribbles, and 2D masks. Notably, SAGA can finish the 3D segmentation within milliseconds, achieving nearly 1000x acceleration compared to previous SOTA.

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Moondream segmenting delivers pixel-accurate vector masks for arms, packages, and conveyor bins. A free, open-source model with state-of-the-art segmentation benchmarks, built for real-world automation.
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Moondream segmenting delivers pixel-accurate vector masks for arms, packages, and conveyor bins. A free, open-source model with state-of-the-art segmentation benchmarks, built for real-world automation.

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Introducing benchmark tracking across all of arXiv With thousands of models and benchmarks out there, it's impossible to track what's actually state-of-the-art We now index results from millions of papers to surface which models are SOTA according to open source research!
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Introducing benchmark tracking across all of arXiv With thousands of models and benchmarks out there, it's impossible to track what's actually state-of-the-art We now index results from millions of papers to surface which models are SOTA according to open source research!

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Gpt 5 vs Grok 4 SVG animation, (same prompt)
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Gpt 5 vs Grok 4 SVG animation, (same prompt)

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Grep just achieved SOTA on the three major deep research benchmarks, beating Perplexity, Google, Nvidia, OpenAI, and Anthropic. We're a two-person founding team.
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Grep just achieved SOTA on the three major deep research benchmarks, beating Perplexity, Google, Nvidia, OpenAI, and Anthropic. We're a two-person founding team.

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Today we're releasing the Segment Anything Model (SAM) — a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from any photo or video + zero-shot transfer to other segmentation tasks ➡️
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Today we're releasing the Segment Anything Model (SAM) — a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from any photo or video + zero-shot transfer to other segmentation tasks ➡️

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3,570,426 views • 3 years ago

Most 3DGS segmentation tools either pre‑train per scene or lock errors into a feature field you can’t undo. ArtisanGS instead turns a few 2D masks into editable 3D object selections via Cutie tracking + black‑box splat aggregation, then lets you iteratively correct mistakes with consistent 2D/3D selection modes. 📗 #NVIDIAResearch paper:
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Most 3DGS segmentation tools either pre‑train per scene or lock errors into a feature field you can’t undo. ArtisanGS instead turns a few 2D masks into editable 3D object selections via Cutie tracking + black‑box splat aggregation, then lets you iteratively correct mistakes with consistent 2D/3D selection modes. 📗 #NVIDIAResearch paper:

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prompt box attachment interaction ⎯⟡°
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prompt box attachment interaction ⎯⟡°

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prompt box attachment interaction ⎯⟡°
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prompt box attachment interaction ⎯⟡°

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you can now generate prompts from images in Krea drag & drop any image into the prompt box -> get a prompt for it
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you can now generate prompts from images in Krea drag & drop any image into the prompt box -> get a prompt for it

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16,807 views • 4 months ago

just updated my Segment Anything 2 (SAM2) Hugging Face space now you can run the model in two modes - box prompt and automatic mask generation; I'll try to add short video segmentation today link to space if you want to have some fun:
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just updated my Segment Anything 2 (SAM2) Hugging Face space now you can run the model in two modes - box prompt and automatic mask generation; I'll try to add short video segmentation today link to space if you want to have some fun:

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16,075 views • 1 year ago

.Groq Inc has introduced prompt caching for the SOTA open-source coding model, Kimi-k2. What does this mean for you? Significantly reduced costs with Kimi-k2 on its fastest provider. Check the price: left side without prompt caching, right side with prompt caching.
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.Groq Inc has introduced prompt caching for the SOTA open-source coding model, Kimi-k2. What does this mean for you? Significantly reduced costs with Kimi-k2 on its fastest provider. Check the price: left side without prompt caching, right side with prompt caching.

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Screen Studio 3.1 is here Introducing Masks: Blur sensitive data or highlight what matters in your recordings. From passwords to API keys - keep your data private while recording. Give it a try and share your feedback!
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RF-DETR just landed to Hugging Face transformers 🥵🔥 sota real-time detection & segmentation models by Roboflow 💜 > play with our real-time demo > fine-tune the models on your use case with our tutorials (takes a toaster's VRAM) > or just hand them to your agents 😄
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🚀A Simple Baseline for Streaming Video Understanding🚀 #SimpleStream reveals a simple baseline of *recent N-frame sliding window* beats SOTA memory-based methods on standard streaming video benchmarks lmms-lab - Project: - Code:
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