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Huge thanks to MLON_Global and @MLON_Official for sponsoring this video! 🥰 Use code "EC" for 15% off on your next purchase! 🎙️Girl: 🍒 CherryRuby_VA🎙🔞 (COMMISSIONS OPEN) 🔊SFX: 🔊RandomTide🔞 #Rule34 #NSFW #3Dhentai #hentai #3D #tentacle #Mlondoll

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EroChanFX🔞

170,986 subscribers

480,934 Aufrufe • vor 2 Jahren •via X (Twitter)

NSFW#Rule34#NSFW#3Dhentai#hentai#3D#tentacle#Mlondoll
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Meta Alchemist

82,000 Aufrufe • vor 6 Monaten

🚨 SIGGRAPH Asia 2025 Paper Alert 🚨 ➡️Paper Title: WorldExplorer: Towards Generating Fully Navigable 3D Scenes 🌟Few pointers from the paper 🎯Generating 3D worlds from text is a highly anticipated goal in computer vision. Existing works are limited by the degree of exploration they allow inside of a scene, i.e., produce stretched-out and noisy artifacts when moving beyond central or panoramic perspectives. 🎯 To this end, authors of this paper proposed “WorldExplorer”, a novel method based on autoregressive video trajectory generation, which builds fully navigable 3D scenes with consistent visual quality across a wide range of viewpoints. 🎯They initialize their scenes by creating multi-view consistent images corresponding to a 360 degree panorama. 🎯Then, they expanded it by leveraging video diffusion models in an iterative scene generation pipeline. 🎯Concretely, they generated multiple videos along short, pre-defined trajectories, that explore the scene in depth, including motion around objects. 🎯Their novel scene memory conditions each video on the most relevant prior views, while a collision-detection mechanism prevents degenerate results, like moving into objects. 🎯Finally,they fuse all generated views into a unified 3D representation via 3D Gaussian Splatting optimization. 🎯Compared to prior approaches, WorldExplorer produces high-quality scenes that remain stable under large camera motion, enabling for the first time realistic and unrestricted exploration. 🎯They believe this marks a significant step toward generating immersive and truly explorable virtual 3D environments. 🏢Organization: TU München 🧙Paper Authors: Manuel-Andreas Schneider, Lukas Höllein , Matthias Niessner 📝 Read the Full Paper here: 🗂️ Project Page: 🧑‍💻 Code: 🎥 Be sure to watch the attached Technical Summary Video - Sound on 🔊🔊 Find this Valuable 💎 ? ♻️QT and teach your network something new Follow me 👣, naveen manwani , for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #SIGGRAPHAsia2025
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🚨 SIGGRAPH Asia 2025 Paper Alert 🚨 ➡️Paper Title: WorldExplorer: Towards Generating Fully Navigable 3D Scenes 🌟Few pointers from the paper 🎯Generating 3D worlds from text is a highly anticipated goal in computer vision. Existing works are limited by the degree of exploration they allow inside of a scene, i.e., produce stretched-out and noisy artifacts when moving beyond central or panoramic perspectives. 🎯 To this end, authors of this paper proposed “WorldExplorer”, a novel method based on autoregressive video trajectory generation, which builds fully navigable 3D scenes with consistent visual quality across a wide range of viewpoints. 🎯They initialize their scenes by creating multi-view consistent images corresponding to a 360 degree panorama. 🎯Then, they expanded it by leveraging video diffusion models in an iterative scene generation pipeline. 🎯Concretely, they generated multiple videos along short, pre-defined trajectories, that explore the scene in depth, including motion around objects. 🎯Their novel scene memory conditions each video on the most relevant prior views, while a collision-detection mechanism prevents degenerate results, like moving into objects. 🎯Finally,they fuse all generated views into a unified 3D representation via 3D Gaussian Splatting optimization. 🎯Compared to prior approaches, WorldExplorer produces high-quality scenes that remain stable under large camera motion, enabling for the first time realistic and unrestricted exploration. 🎯They believe this marks a significant step toward generating immersive and truly explorable virtual 3D environments. 🏢Organization: TU München 🧙Paper Authors: Manuel-Andreas Schneider, Lukas Höllein , Matthias Niessner 📝 Read the Full Paper here: 🗂️ Project Page: 🧑‍💻 Code: 🎥 Be sure to watch the attached Technical Summary Video - Sound on 🔊🔊 Find this Valuable 💎 ? ♻️QT and teach your network something new Follow me 👣, naveen manwani , for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #SIGGRAPHAsia2025

naveen manwani

10,578 Aufrufe • vor 10 Monaten

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. ​ Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. ​ If you are a Python developer, there is an open-source alternative! ​ Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. ​ Quix Streams is all Python, and it's open-source! ​ The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. ​ Here is what the video covers: ​ • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) ​ • How to efficiently stream that data into Kafka ​ • How to optimize Kafka producer settings like batch size and compression for maximum throughput ​ A few other notes about Quix Streams: ​ • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity ​ You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. ​ Thanks to the Quix team for collaborating with me on this post.
27:43

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. ​ Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. ​ If you are a Python developer, there is an open-source alternative! ​ Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. ​ Quix Streams is all Python, and it's open-source! ​ The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. ​ Here is what the video covers: ​ • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) ​ • How to efficiently stream that data into Kafka ​ • How to optimize Kafka producer settings like batch size and compression for maximum throughput ​ A few other notes about Quix Streams: ​ • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity ​ You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. ​ Thanks to the Quix team for collaborating with me on this post.

Santiago

89,230 Aufrufe • vor 2 Jahren