Search Results for "2d 3d plotter"
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Showing 45 open source projects for "2d 3d plotter"
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InsightFace
State-of-the-art 2D and 3D Face Analysis Project
State-of-the-art deep face analysis library. InsightFace is an open-source 2D&3D deep face analysis library. InsightFace is an integrated Python library for 2D&3D face analysis. InsightFace efficiently implements a wide variety of state-of-the-art algorithms for face recognition, face detection, and face alignment, which are optimized for both training and deployment. Research institutes and industrial organizations can get benefits from InsightFace library. -
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Step1X-3D
High-Fidelity and Controllable Generation of Textured 3D Assets
Step1X-3D is an open-source framework for generating high-fidelity textured 3D assets from scratch — both their geometry and surface textures — using modern generative AI techniques. It combines a hybrid architecture: a geometry generation stage using a VAE-DiT model to output a watertight 3D representation (e.g. TSDF surface), and a texture synthesis stage that conditions on geometry and optionally reference input (or prompts) to produce view-consistent textures using a diffusion-based... -
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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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Mesh R-CNN
code for Mesh R-CNN, ICCV 2019
Mesh R-CNN is a 3D reconstruction and object understanding framework developed by Facebook Research that extends Mask R-CNN into the 3D domain. Built on top of Detectron2 and PyTorch3D, Mesh R-CNN enables end-to-end 3D mesh prediction directly from single RGB images. The model learns to detect, segment, and reconstruct detailed 3D mesh representations of objects in natural images, bridging the gap between 2D perception and 3D understanding. ... -
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MESHROOM
3D reconstruction software
Photogrammetry is the science of making measurements from photographs. It infers the geometry of a scene from a set of unordered photographies or videos. Photography is the projection of a 3D scene onto a 2D plane, losing depth information. The goal of photogrammetry is to reverse this process. The dense modeling of the scene is the result yielded by chaining two computer vision-based pipelines, “Structure-from-Motion” (SfM) and “Multi View Stereo” (MVS). Fusion of Multi-bracketing LDR images into HDR. Alignment of panorama images. ... -
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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, AR/VR content creation, robotics perception, and 3D reconstruction workflows, making it versatile across industries and research domains. ... -
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PyTorch3D
PyTorch3D is FAIR's library of reusable components for deep learning
...Researchers use it for tasks like shape generation, reconstruction, view synthesis, and visual reasoning. PyTorch3D also includes utilities for loading, transforming, and sampling 3D assets, so models can be trained end-to-end from 2D supervision or partial data. Its modular design allows easy extension—components like differentiable rasterizers, mesh blending, or signed distance field (SDF) modules can be swapped or combined to test new architectures quickly. -
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OpenClaw Office
OpenClaw Office is the visual monitoring and management frontend
OpenClaw Office is a visual monitoring and management interface designed for the OpenClaw multi-agent system, providing an immersive and interactive way to observe and control autonomous AI agents. It presents agent activity through a virtual office environment, where each agent is represented as an animated entity within a 2D or 3D workspace. The platform enables real-time visualization of agent states, interactions, and workflows, making complex multi-agent coordination easier to understand and debug. Users can observe communication flows between agents through visual connections, track token usage and operational costs, and analyze performance through integrated dashboards and charts. ... -
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AliceVision
3D Computer Vision Framework
AliceVision is an open-source photogrammetric computer vision framework designed to reconstruct detailed 3D scenes and camera motion from collections of images or videos. It provides a complete pipeline for structure-from-motion (SfM), multi-view stereo (MVS), and mesh generation, allowing users to convert 2D imagery into accurate 3D models. The framework is built with a strong emphasis on research-grade algorithms while maintaining the robustness required for production environments, making it suitable for industries such as visual effects, cultural heritage preservation, and robotics. ... -
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Video Diffusion - Pytorch
Implementation of Video Diffusion Models
...Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch. It uses a special space-time factored U-net, extending generation from 2D images to 3D videos. 14k for difficult moving mnist (converging much faster and better than NUWA) - wip. Any new developments for text-to-video synthesis will be centralized at Imagen-pytorch. For conditioning on text, they derived text embeddings by first passing the tokenized text through BERT-large. You can also directly pass in the descriptions of the video as strings, if you plan on using BERT-base for text conditioning. ... -
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DeepLabCut
Implementation of DeepLabCut
DeepLabCut™ is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. -
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Minigrid
Simple and easily configurable grid world environments
...Because of its simplicity, it is often used for rapid prototyping, analytic experiments, curriculum learning, or pedagogical tutorials. While it is not a full 3D simulation environment, its strength lies in enabling many environment resets and steps cheaply, which is valuable for algorithmic RL research rather than high-fidelity rendering. -
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Tracking Any Point (TAP)
DeepMind model for tracking arbitrary points across videos & robotics
TAPNet is the official Google DeepMind repository for Tracking Any Point (TAP), bundling datasets, models, benchmarks, and demos for precise point tracking in videos. The project includes the TAP-Vid and TAPVid-3D benchmarks, which evaluate long-range tracking of arbitrary points in 2D and 3D across diverse real and synthetic videos. Its flagship models—TAPIR, BootsTAPIR, and the latest TAPNext—use matching plus temporal refinement or next-token style propagation to achieve state-of-the-art accuracy and speed on TAP-Vid. RoboTAP demonstrates how TAPIR-style tracks can drive real-world robot manipulation via efficient imitation, and ships with a dataset of annotated robotics videos. ... -
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Make-A-Video - Pytorch (wip)
Implementation of Make-A-Video, new SOTA text to video generator
...Passing in images (if one were to pretrain on images first), both temporal convolution and attention will be automatically skipped. In other words, you can use this straightforwardly in your 2d Unet and then port it over to a 3d Unet once that phase of the training is done. -
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Qwen3-VL
Qwen3-VL, the multimodal large language model series by Alibaba Cloud
Qwen3-VL is the latest multimodal large language model series from Alibaba Cloud’s Qwen team, designed to integrate advanced vision and language understanding. It represents a major upgrade in the Qwen lineup, with stronger text generation, deeper visual reasoning, and expanded multimodal comprehension. The model supports dense and Mixture-of-Experts (MoE) architectures, making it scalable from edge devices to cloud deployments, and is available in both instruction-tuned and... -
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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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Face Alignment
2D and 3D Face alignment library build using pytorch
Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. More models will be added soon. By default, the package will use the SFD face detector. However, the users can alternatively use dlib, BlazeFace, or pre-existing ground truth bounding boxes. ... -
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Stable-Dreamfusion
Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion
A pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model. This project is a work-in-progress and contains lots of differences from the paper. The current generation quality cannot match the results from the original paper, and many prompts still fail badly! Since the Imagen model is not publicly available, we use Stable Diffusion to replace it (implementation from diffusers). -
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PIFuHD
High-Resolution 3D Human Digitization from A Single Image
PIFuHD (Pixel-Aligned Implicit Function for 3D human reconstruction at high resolution) is a method and codebase to reconstruct high-fidelity 3D human meshes from a single image. It extends prior PIFu work by increasing resolution and detail, enabling fine geometry in cloth folds, hair, and subtle surface features. The method operates by learning an implicit occupancy / surface function conditioned on the image and camera projection; at inference time it queries dense points to reconstruct a... -
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Point-E
Point cloud diffusion for 3D model synthesis
point-e is the official repository for Point-E, a generative model developed by OpenAI that produces 3D point clouds from textual (or image) prompts. Its principal advantage is speed: it can generate 3D assets in just 1–2 minutes on a single GPU, which is significantly faster than many competing text-to-3D models. The model works via a two-stage diffusion approach: first, it uses a text → image diffusion network to produce a synthetic 2D view consistent with the prompt; then a second diffusion model converts that image into a 3D point cloud. ... -
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FrankMocap
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
FrankMocap is a monocular 3D human capture system that estimates body, hand, and optionally face pose from a single RGB image or video. It regresses parametric human models (e.g., SMPL/SMPL-X) directly, producing temporally stable meshes and joint angles suitable for animation or analytics. The pipeline couples a robust 2D keypoint detector with 3D mesh regression networks and priors that keep results anatomically plausible. -
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Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no... -
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AI Atelier
Based on the Disco Diffusion, version of the AI art creation software
...Copyright and license notices must be preserved. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available. Create 2D and 3D animations and not only still frames (from Disco Diffusion v5 and VQGAN Animations). Input audio and images for generation instead of just text. Simplify tool setup process on colab, and enable ‘one-click’ sharing of the generated link to other users. Experiment with the possibilities for multi-user access to the same link. -
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VRN
Code for "Large Pose 3D Face Reconstruction
The VRN (Volumetric Regression Network) repository implements the “Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression” method. Instead of explicitly fitting a 3D model via landmark estimation and deformation, VRN treats the reconstruction task as volumetric segmentation: it learns a CNN to regress a 3D volume aligned to the input image, and then extracts a mesh via isosurface from that volume. -
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DensePose
A real-time approach for mapping all human pixels of 2D RGB images
...DensePose is widely used in augmented reality, motion capture, virtual try-on, and visual effects applications because it enables real-time 3D human mapping from 2D inputs. The model architecture builds on Mask R-CNN, using additional regression heads to predict UV coordinates that map image pixels to 3D surfaces.
