Search Results for "3d game python"
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68 projects for "3d game python" with 2 filters applied:
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SAM 3D Objects
Models for object and human mesh reconstruction
SAM 3D Objects is a foundation model that reconstructs full 3D geometry, texture, and spatial layout of objects and scenes from a single image. Given one RGB image and object masks (for example, from the Segment Anything family), it can generate a textured 3D mesh for each object, including pose and approximate scene layout. The model is specifically designed to be robust in real-world images with clutter, occlusions, small objects, and unusual viewpoints, where many earlier 3D-from-image... -
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SAM 3D Body
Code for running inference with the SAM 3D Body Model 3DB
...The model is trained to be robust in diverse, in-the-wild conditions, so it handles varied clothing, viewpoints, and backgrounds while maintaining strong accuracy across multiple human-pose benchmarks. The repository provides Python code to run inference, utilities to download checkpoints from Hugging Face, and demo scripts that turn images into 3D meshes and visualizations. There are Jupyter notebooks that walk you through setting up the model, running it on example images, and visualizing outputs in 3D, making it approachable even if you are not a 3D expert. -
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ComfyUI-3D-Pack
An extensive node suite that enables ComfyUI to process 3D inputs
ComfyUI-3D-Pack is an extension package for the ComfyUI visual AI workflow environment that enables users to generate and manipulate 3D assets using advanced machine learning techniques. ComfyUI itself is a node-based interface for designing and executing generative AI pipelines, and this extension expands its capabilities by introducing nodes specifically designed for working with three-dimensional data. The package allows the platform to process inputs such as meshes and UV textures and... -
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DreamCraft3D
Official implementation of DreamCraft3D
DreamCraft3D is DeepSeek’s generative 3D modeling framework / model family that likely extends their earlier 3D efforts (e.g. Shap-E or Point-E style models) with more capability, control, or expression. The name suggests a “dream crafting” metaphor—users probably supply textual or image prompts and generate 3D assets (point clouds, meshes, scenes). The repository includes model code, inference scripts, sample prompts, and possibly dataset preparation pipelines. It may integrate rendering or... -
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Hunyuan3D-2.1
From Images to High-Fidelity 3D Assets
Hunyuan3D-2.1 is Tencent Hunyuan’s advanced 3D asset generation system that produces high-fidelity 3D models with Physically Based Rendering (PBR) textures. It is fully open-source with released model weights, training, and inference code. It improves on prior versions by using a PBR texture pipeline (enabling realistic material effects like reflections and subsurface scattering) and allowing community fine-tuning and extension. -
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Godot RL Agents
An Open Source package that allows video game creators
godot_rl_agents is a reinforcement learning integration for the Godot game engine. It allows AI agents to learn how to interact with and play Godot-based games using RL algorithms. The toolkit bridges Godot with Python-based RL libraries like Stable-Baselines3, making it possible to create complex and visually rich RL environments natively in Godot. -
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Hunyuan3D-1
A Unified Framework for Text-to-3D and Image-to-3D Generation
Hunyuan3D-1 is an earlier version in the same 3D generation line (the unified framework for text-to-3D and image-to-3D tasks) by Tencent Hunyuan. It provides a framework combining shape generation and texture synthesis, enabling users to create 3D assets from images or text conditions. While less advanced than version 2.1, it laid the foundations for the later PBR, higher resolution, and open-source enhancements. (Note: less detailed public documentation was found for Hunyuan3D-1 compared to... -
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LLaMA-Mesh
Unifying 3D Mesh Generation with Language Models
LLaMA-Mesh is a research framework that extends large language models so they can understand and generate 3D mesh data alongside text. The system introduces a method for representing 3D meshes in a textual format by encoding vertex coordinates and face definitions as sequences that can be processed by a language model. By serializing 3D geometry into text tokens, the approach allows existing transformer architectures to generate and interpret 3D models without requiring specialized visual... -
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PyTorch3D
PyTorch3D is FAIR's library of reusable components for deep learning
PyTorch3D is a comprehensive library for 3D deep learning that brings differentiable rendering, geometric operations, and 3D data structures into the PyTorch ecosystem. It’s designed to make it easy to build and train neural networks that work directly with 3D data such as meshes, point clouds, and implicit surfaces. The library provides fast GPU-accelerated implementations of rendering pipelines, transformations, rasterization, and lighting—making it possible to compute gradients through... -
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WorldGen
Generate Any 3D Scene in Seconds
WorldGen is an AI model and library that can generate full 3D scenes in a matter of seconds from either text prompts or reference images. It is designed to create interactive environments suitable for games, simulations, robotics research, and virtual reality, rather than just static 3D assets. The core idea is that you describe a world in natural language and WorldGen produces a navigable 3D scene that you can freely explore in 360 degrees, with loop closure so that the space remains... -
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HunyuanWorld-Voyager
RGBD video generation model conditioned on camera input
HunyuanWorld-Voyager is a next-generation video diffusion framework developed by Tencent-Hunyuan for generating world-consistent 3D scene videos from a single input image. By leveraging user-defined camera paths, it enables immersive scene exploration and supports controllable video synthesis with high realism. The system jointly produces aligned RGB and depth video sequences, making it directly applicable to 3D reconstruction tasks. At its core, Voyager integrates a world-consistent video... -
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HY-World 2.0
A Multi-Modal World Model for Reconstructing, Generating, Simulation
HY-World 2.0 is a multi-modal world model framework for reconstructing, generating, and simulating navigable 3D worlds from diverse inputs. It accepts text prompts, single-view images, multi-view images, and videos, and produces 3D world representations rather than limiting output to flat video generation. For text and single-image inputs, it generates high-fidelity 3D Gaussian Splatting scenes through a multi-stage pipeline that includes panorama generation, trajectory planning, world... -
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Lyra 2
Project Lyra: Open Generative 3D World Models
The Lyra 2 project is a research-driven framework developed by NVIDIA that focuses on building open generative 3D world models using advanced diffusion-based techniques. It enables the creation of fully explorable 3D environments from minimal inputs such as a single image or video, leveraging self-distillation methods to generate consistent spatial representations. The system evolves across versions, with newer iterations introducing long-horizon generation and improved 3D consistency across... -
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HY-World 1.5
A Systematic Framework for Interactive World Modeling
HY-WorldPlay is a Hunyuan AI project focusing on immersive multimodal content generation and interaction within virtual worlds or simulated environments. It aims to empower AI agents with the capability to both understand and generate multimedia content — including text, audio, image, and potentially 3D or game-world elements — enabling lifelike dialogue, environmental interpretations, and responsive world behavior. The platform targets use cases in digital entertainment, game worlds, training simulators, and interactive storytelling, where AI agents need to adapt to real-time user inputs and changes in environment state. It blends advanced reasoning with multimodal synthesis, enabling agents to describe scenes, generate context-appropriate responses, and contribute to narrative or gameplay flows. ... -
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Depth Anything 3
Recovering the Visual Space from Any Views
Depth Anything 3 is a research-driven project that brings accurate and dense depth estimation to any input image or video, enabling foundational understanding of 3D structure from 2D visual content. Designed to work across diverse scenes, lighting conditions, and image types, it uses advanced neural networks trained on large, heterogeneous datasets, producing depth maps that reveal scene depth relationships and object surfaces with strong fidelity. The model can be applied to photography,... -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. Using this dataset, the project constructs matchup features that represent team performance trends and contextual information about each game. -
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Depth Pro
Sharp Monocular Metric Depth in Less Than a Second
Depth Pro is a foundation model for zero-shot metric monocular depth estimation, producing sharp, high-frequency depth maps with absolute scale from a single image. Unlike many prior approaches, it does not require camera intrinsics or extra metadata, yet still outputs metric depth suitable for downstream 3D tasks. Apple highlights both accuracy and speed: the model can synthesize a ~2.25-megapixel depth map in around 0.3 seconds on a standard GPU, enabling near real-time applications. The... -
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Map-Anything
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
Map-Anything is a universal, feed-forward transformer for metric 3D reconstruction that predicts a scene’s geometry and camera parameters directly from visual inputs. Instead of stitching together many task-specific models, it uses a single architecture that supports a wide range of 3D tasks—multi-image structure-from-motion, multi-view stereo, monocular metric depth, registration, depth completion, and more. The model flexibly accepts different input combinations (images, intrinsics, poses,... -
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TextWorld
TextWorld is a sandbox learning environment for the training
TextWorld is a learning environment designed to train reinforcement learning agents to play text-based games, where actions and observations are entirely in natural language. Developed by Microsoft Research, TextWorld focuses on language understanding, planning, and interaction in complex, narrative-driven environments. It generates games procedurally, enabling scalable testing of agents’ natural language processing and decision-making abilities. -
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video2robot
End-to-end pipeline converting generative videos
video2robot is an end-to-end open-source pipeline that converts generative video or prompt-driven motion content into executable humanoid robot motion sequences, enabling researchers and developers to go from high-level action descriptions or videos to robot-ready motion data. The pipeline supports both prompt-to-video generation using models like Veo/Sora and video upload processing, followed by human pose extraction through a 3D pose model and retargeting of that motion to robot joints... -
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Sunfish
Sunfish: a Python Chess Engine in 111 lines of code
...The project is often used as an educational tool for understanding game AI, search algorithms, and evaluation functions without the complexity of larger engines. It includes a simple UCI-compatible interface, allowing it to be integrated with graphical chess interfaces or used in terminal-based gameplay. The codebase is intentionally minimal, making it ideal for experimentation, modification, and learning purposes. -
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LLM Colosseum
Benchmark LLMs by fighting in Street Fighter 3
LLM-Colosseum is an experimental benchmarking framework designed to evaluate the capabilities of large language models through gameplay interactions rather than traditional text-based benchmarks. The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making... -
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GalTransl
Automated translation solution for visual novels
GalTransl is an automated translation system specifically designed for visual novels, particularly those in the “galgame” genre, leveraging large language models to streamline and enhance the translation process. It integrates support for multiple advanced LLM providers such as GPT-4, Claude, DeepSeek, and other models, enabling high-quality, context-aware translations that go beyond traditional machine translation approaches. The platform is built to handle the unique structure of visual... -
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OSWorld
Benchmarking Multimodal Agents for Open-Ended Tasks
OSWorld is an open-source synthetic world environment designed for embodied AI research and multi-agent learning. It provides a richly simulated 3D world where multiple agents can interact, perform tasks, and learn complex behaviors. OSWorld emphasizes multi-modal interaction, enabling agents to process visual, auditory, and symbolic data for grounded learning in a simulated world. -
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ManiSkill
SAPIEN Manipulation Skill Framework
ManiSkill is a benchmark platform for training and evaluating reinforcement learning agents on dexterous manipulation tasks using physics-based simulations. Developed by Hao Su Lab, it focuses on robotic manipulation with diverse, high-quality 3D tasks designed to challenge perception, control, and planning in robotics. ManiSkill provides both low-level control and visual observation spaces for realistic learning scenarios.
