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Introducing Gemini Omni

OpenAIJune 1, 20266Views0Likes0Comments
Last year, Nano Banana brought Gemini's intelligence to image generation and editing. Since then, it’s helped millions of people restore old photos, design from sketches and visualize ideas in ways that weren’t possible before. From the start we built Gemini to be natively multimodal from the ground up, and now we’re taking the next step.…

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Genesis AI Releases Nyx, Quadrants, and Genesis World 1.0 Physics Platform for Scalable Robotics Foundation Model Evaluation

RoboticsJune 1, 20267Views0Likes0Comments
Genesis AI released Genesis World 1.0. The platform consists of four components: the Genesis World physics engine, Nyx (a real-time path-traced renderer), Quadrants (a Python-to-GPU compiler), and a simulation interface. It is designed to accelerate robotics foundation model development through simulation-based evaluation. Robotics model development has two bottlenecks: data and iteration speed. The field has…

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Meta AI Releases Sapiens2: A High-Resolution Human-Centric Vision Model for Pose, Segmentation, Normals, Pointmap, and Albedo

AI NewsMay 27, 20267Views0Likes0Comments
If you’ve ever watched a motion capture system struggle with a person’s fingers, or seen a segmentation model fail to distinguish teeth from gums, you already understand why human-centric computer vision is hard. Humans are not just objects, they come with articulated structure, fine surface details, and enormous variation in pose, clothing, lighting, and ethnicity.…

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

RoboticsMay 27, 20269Views0Likes0Comments
import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from pathlib import Path import re def parse_trc(trc_path): """Parse a .trc file and return marker names, frame data, and metadata.""" with open(trc_path, 'r') as f: lines = f.readlines() meta_keys = lines[2].strip().split('\t') meta_vals = lines[3].strip().split('\t') …

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How to Build a Lightweight Vision-Language-Action-Inspired Embodied Agent with Latent World Modeling and Model Predictive Control

AI NewsMay 22, 20266Views0Likes0Comments
import random, numpy as np, torch, torch.nn as nn, torch.nn.functional as F import matplotlib.pyplot as plt from dataclasses import dataclass from typing import Tuple, Dict, List from torch.utils.data import Dataset, DataLoader try: from tqdm.auto import tqdm except Exception: def tqdm(x, **kwargs): return x SEED = 7 random.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED) if device.type == "cuda": torch.backends.cudnn.benchmark = True @dataclass class WorldConfig: …

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