Computer vision

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Google DeepMind Releases Gemini Robotics-ER 1.6: Bringing Enhanced Embodied Reasoning and Instrument Reading to Physical AI

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Google DeepMind research team introduced Gemini Robotics-ER 1.6, a significant upgrade to its embodied reasoning model designed to serve as the ‘cognitive brain’ of robots operating in real-world environments. The model specializes in reasoning capabilities critical for robotics, including visual and spatial understanding, task planning, and success detection — acting as the high-level reasoning model […]

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A Coding Implementation of MolmoAct for Depth-Aware Spatial Reasoning, Visual Trajectory Tracing, and Robotic Action Prediction

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In this tutorial, we walk through MolmoAct step by step and build a practical understanding of how action-reasoning models can reason in space from visual observations. We set up the environment, load the model, prepare multi-view image inputs, and explore how MolmoAct produces depth-aware reasoning, visual traces, and actionable robot outputs from natural language instructions.

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Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference

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Liquid AI just released LFM2.5-VL-450M, an updated version of its earlier LFM2-VL-450M vision-language model. The new release introduces bounding box prediction, improved instruction following, expanded multilingual understanding, and function calling support — all within a 450M-parameter footprint designed to run directly on edge hardware ranging from embedded AI modules like NVIDIA Jetson Orin, to mini-PC

Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference Read More »

A Coding Guide to Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim

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In this tutorial, we build and run a complete Pose2Sim pipeline on Colab to understand how markerless 3D kinematics works in practice. We begin with environment setup, configure the project for Colab’s headless runtime, and then walk through calibration, 2D pose estimation, synchronization, person association, triangulation, filtering, marker augmentation, and OpenSim-based kinematics. As we progress,

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Meta Superintelligence Lab Releases Muse Spark: A Multimodal Reasoning Model With Thought Compression and Parallel Agents

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Meta Superintelligence Labs recently made a significant move by unveiling ‘Muse Spark’ — the first model in the Muse family. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. https://ai.meta.com/static-resource/muse-spark-eval-methodology What ‘Natively Multimodal’ Actually Means When Meta describes Muse Spark as ‘natively multimodal,’ it means

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Meta AI Releases EUPE: A Compact Vision Encoder Family Under 100M Parameters That Rivals Specialist Models Across Image Understanding, Dense Prediction, and VLM Tasks

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Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that made them useful in the first place. Worse, specialized models tend to excel at one

Meta AI Releases EUPE: A Compact Vision Encoder Family Under 100M Parameters That Rivals Specialist Models Across Image Understanding, Dense Prediction, and VLM Tasks Read More »

How to Build a Netflix VOID Video Object Removal and Inpainting Pipeline with CogVideoX, Custom Prompting, and End-to-End Sample Inference

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In this tutorial, we build and run an advanced pipeline for Netflix’s VOID model. We set up the environment, install all required dependencies, clone the repository, download the official base model and VOID checkpoint, and prepare the sample inputs needed for video object removal. We also make the workflow more practical by allowing secure terminal-style

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Netflix AI Team Just Open-Sourced VOID: an AI Model That Erases Objects From Videos — Physics and All

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Video editing has always had a dirty secret: removing an object from footage is easy; making the scene look like it was never there is brutally hard. Take out a person holding a guitar, and you’re left with a floating instrument that defies gravity. Hollywood VFX teams spend weeks fixing exactly this kind of problem.

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