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Final call for applications for Roboteam program which is ideal for those looking to start a business but who lack coding skills or a team.
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Artificial Intelligence && Deep Learning pinned Β«Join the second round of free startup acceleration program in September 2024 Final call for applications for Roboteam program which is ideal for those looking to start a business but who lack coding skills or a team. Throughout the program, participantsβ¦Β»
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βοΈ BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
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"DARTH: Holistic Test-time Adaptation for Multiple Object Tracking"
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β https://t.me/deeplearning_ai
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βοΈ 3D Reconstruction with Spatial Memory
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* Project Page: Spann3R
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* Paper: Read the paper
β https://t.me/deeplearning_ai
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β https://t.me/deeplearning_ai
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Feel free to reach out for more details!
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βοΈ AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction π₯
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* Paper: Read the paper
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HunyuanVideo: A Systematic Framework For Large Video Generation Model
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β https://t.me/deeplearning_ai
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* Source code: Github
* Project Page: HunyuanVideo
* Paper: Read the paper
β https://t.me/deeplearning_ai
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PanoDreamer: 3D Panorama Synthesis from a Single Image
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* YouTube: Video Results
β https://t.me/deeplearning_ai
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* Source code: Github
* Project Page: PanoDreamer
* Paper: Read the paper
* YouTube: Video Results
β https://t.me/deeplearning_ai
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Elevating Flow-Guided Video Inpainting with Reference Generation
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OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving
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* Paper: Read the paper
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π₯ Shanghai AI Lab Introduces RTMW: Real-Time Multi-Person Whole-Body Pose Estimation π₯
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π Discover More:
* Source code: Github
* Paper: Read the paper
β https://t.me/deeplearning_ai
β’ Blazing Speed: 940+ FPS on GPU, 50β300+ FPS on CPU
β’ High-Performance Models: Supports 2D/3D whole-body pose estimation
β’ Cutting-Edge Architecture: Built upon RTMPose with FPN & HEM
β’ Wide Dataset Coverage: Manually aligned keypoints across 14 open datasets
β’ Extensive Framework Support: ONNX, TensorRT, ncnn, and OpenVINO
β’ Open Source: Code and pre-trained models available
π Discover More:
* Source code: Github
* Paper: Read the paper
β https://t.me/deeplearning_ai