Search Results for "scene" - Page 2
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Showing 74 open source projects for "scene"
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MCP for Unity
AI bridge enabling assistants to control and automate Unity Editor
Unity MCP is an open source integration that connects AI assistants with the Unity Editor through the Model Context Protocol (MCP). It acts as a bridge that allows language models and AI coding tools to interact directly with a Unity development environment using structured commands and tools. By linking an AI assistant to a running Unity project, the system enables automated operations such as managing project assets, modifying scenes, editing scripts, and performing other development tasks... -
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Depth Anything 3
Recovering the Visual Space from Any Views
...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. It includes support for high-resolution inputs and post-processing tools that refine depth predictions, helping downstream tasks like segmentation, bounding volume estimation, and mixed reality layering. -
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Depth Pro
Sharp Monocular Metric Depth in Less Than a Second
...Community integrations (e.g., inference wrappers and UI nodes) have sprung up around the model, reflecting practical interest in video, AR, and generative pipelines. As a general-purpose monocular depth backbone, Depth Pro slots into 3D reconstruction, relighting, and scene understanding workflows that benefit from metric predictions. -
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DeepSeek VL2
Mixture-of-Experts Vision-Language Models for Advanced Multimodal
DeepSeek-VL2 is DeepSeek’s vision + language multimodal model—essentially the next-gen successor to their first vision-language models. It combines image and text inputs into a unified embedding / reasoning space so that you can query with text and image jointly (e.g. “What’s going on in this scene?” or “Generate a caption appropriate to context”). The model supports both image understanding (vision tasks) and multimodal reasoning, and is likely used as a component in agent systems to process visual inputs as context for downstream tasks. The repository includes evaluation results (e.g. image/text alignment scores, common VL benchmarks), configuration files, and model weights (where permitted). ... -
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claude-video
Give Claude the ability to watch any video
Claude Video is an agent skill that gives Claude and compatible coding assistants the ability to analyze video content. It accepts public video URLs or local video files, then extracts the information needed to answer user questions about what happened on screen and in the audio. The workflow checks captions first, downloads only what is necessary, extracts timestamped frames, and produces a transcript through native captions or Whisper fallback. It supports different detail levels so users... -
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AutoCrop-Vertical
Smart video converter using YOLOv8 and FFmpeg
...Instead of applying a static center crop, the system intelligently tracks people or key objects to preserve visual focus and composition. When cropping would degrade the scene, it can switch to alternative layouts such as letterboxing to maintain context. The tool integrates FFmpeg for encoding and rendering, ensuring efficient processing and compatibility with standard video workflows. It supports multiple output aspect ratios and quality settings, allowing customization for different platforms. Designed as a lightweight script, it runs locally and provides a cost-free alternative to commercial video reframing tools. -
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VOID
Video Object and Interaction Deletion
...Built on top of transformer-based architectures and fine-tuned for video inpainting tasks, the system uses interaction-aware mask conditioning to ensure temporal consistency across frames. One of its most notable capabilities is its ability to simulate realistic scene behavior after object removal, such as causing an object to fall naturally if its support is removed, which significantly enhances realism. -
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Grounded-Segment-Anything
Marrying Grounding DINO with Segment Anything & Stable Diffusion
...This fusion lets users provide arbitrary text descriptions (e.g., “a cat, a bicycle, or a coffee mug”), have the detection model find relevant bounding boxes, and then use SAM to generate precise segmentation masks that isolate each object in the scene. -
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InstantCharacter
Personalize Any Characters with a Scalable Diffusion Transformer
...It works by adapting a base image generation model with a lightweight adapter so that you can produce character-preserving generations in various downstream tasks (e.g. changing pose, clothing, scene) without needing full model fine-tuning. Works with huggingface/transformers/diffusers ecosystems. -
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Qwen-2.5-VL
Qwen2.5-VL is the multimodal large language model series
Qwen2.5 is a series of large language models developed by the Qwen team at Alibaba Cloud, designed to enhance natural language understanding and generation across multiple languages. The models are available in various sizes, including 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameters, catering to diverse computational requirements. Trained on a comprehensive dataset of up to 18 trillion tokens, Qwen2.5 models exhibit significant improvements in instruction following, long-text generation... -
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Sygil WebUI
Stable Diffusion web UI
Sygil WebUI is a browser-based interface for running Stable Diffusion image generation locally or on a server, wrapping common text-to-image and image-to-image workflows into a practical UI. It provides multiple UI modes (including a legacy Gradio interface) and focuses on making iterative prompting, parameter tuning, and post-processing accessible without writing code. The UI exposes core generation controls like resolution, CFG guidance, sampling steps, samplers, seeds, and batch... -
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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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HunyuanVideo-Avatar
Tencent Hunyuan Multimodal diffusion transformer (MM-DiT) model
...Innovations include a character image injection module, an Audio Emotion Module for transferring emotion cues, and a Face-Aware Audio Adapter to isolate audio effects on faces, enabling multiple characters to be animated in a scene. Character image injection module for better consistency between training and inference conditioning. Emotion control by extracting emotion reference images and transferring emotional style into video sequences. -
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Sa2VA
Official Repo For "Sa2VA: Marrying SAM2 with LLaVA
...It merges the segmentation power of a state-of-the-art video segmentation model (based on SAM‑2) with the vision-language reasoning capabilities of a strong LLM backbone (derived from models like InternVL2.5 / Qwen-VL series), yielding a system that can answer questions about visual content, perform referring segmentation, and maintain temporal consistency across frames in video. With minimal instruction tuning (often one-shot), Sa2VA can handle tasks such as “segment the main subject,” “what are the objects in this scene?”, or “track this object through the video,” outputting pixel-perfect masks or spoken/textual answers as appropriate. -
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USO
Open-sourced unified customization model
USO is ByteDance’s “Unified Style and Subject-Driven Generation” framework, open-sourced to allow customization in generative modeling by disentangling style and subject representation and using reward learning to guide generation. The system is designed such that users can control both “what” is generated (the subject: e.g. a person, object, scene) and “how” it is generated (the style: artistic style, color palette, aesthetic) separately, giving much more flexibility than conventional monolithic generative models. By decoupling style and subject, USO enables reuse of learned style/style-embeddings across different subjects, or vice versa, which makes generation more modular and controllable. ... -
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VGGSfM
VGGSfM: Visual Geometry Grounded Deep Structure From Motion
...The system combines learned feature matching and geometric optimization to generate high-quality camera calibrations, sparse/dense point clouds, and depth maps in standard COLMAP format. Version 2.0 adds support for dynamic scene handling, dense point cloud export, video-based reconstruction (1000+ frames), and integration with Gaussian Splatting pipelines. It leverages tools like PyCOLMAP, poselib, LightGlue, and PyTorch3D for feature matching, pose estimation, and visualization. With minimal configuration, users can process single scenes or full video sequences, apply motion masks to exclude moving objects, and train neural radiance or splatting models directly from reconstructed outputs. -
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SAHI
A lightweight vision library for performing large object detection
...However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities. Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. -
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FreeCAD
a parametric 3D CAD modeler
...It is 100% Open Source and extremely modular, allowing for very advanced extension and customization. FreeCAD is based on OpenCasCade, a powerful geometry kernel, features an Open Inventor-compliant 3D scene representation model provided by the Coin 3D library, and a broad Python API. The interface is built with Qt. FreeCAD runs exactly the same way on Windows, Mac OSX and Linux platforms. -
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WhisperJAV
A subtitle generator for Japanese Adult Videos.
A subtitle generator for Japanese Adult Videos. Transformer-based ASR architectures like Whisper suffer significant performance degradation when applied to the spontaneous and noisy domain of JAV. This degradation is driven by specific acoustic and temporal characteristics that defy the statistical distributions of standard training data. -
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PyCAPGE
PyCAPGE - Python Classic Adventure Point and Click Game Engine
PyCAPGE (Python Classic Adventure Point and Click Game Engine) is a versatile, open-source framework designed for creating retro-style 2D graphic adventures using Python and Pygame. Inspired by the golden age of SCUMM games, it features a customizable 9-verb interface and robust inventory management. Key features include a Scene Manager supporting parallax scrolling, walk-behind masks, and depth-based character scaling. It implements intelligent Pathfinding to navigate complex environments automatically. The engine natively supports Multi-Character gameplay, allowing dynamic switching between protagonists. Developers can build rich narratives using the branching Dialogue System and a Cutscene Manager for scripted events. ... -
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Nougat
Implementation of Nougat Neural Optical Understanding
Nougat is a multi-modal generative modeling framework that bridges vision and text modalities with structured generation control (e.g. layout, scene composition) rather than treating images as flat contexts. It combines object-centric modules with transformer-based reasoning to propose, refine, and render scenes in a generative pipeline. The architecture allows you to specify or prompt a layout (which objects should be where) and then the model fills in appearance, context, lighting, and relations coherently. ... -
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NeuMan
Neural Human Radiance Field from a Single Video (ECCV 2022)
NeuMan is a reference implementation that reconstructs both an animatable human and its background scene from a single monocular video using neural radiance fields. It supports novel view and novel pose synthesis, enabling compositional results like transferring reconstructed humans into new scenes. The pipeline separates human/body and environment, learning consistent geometry and appearance to support animation. Demos showcase sequences such as dance and handshake, and the code provides guidance for running evaluations and rendering. ... -
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BCI
BCI: Breast Cancer Immunohistochemical Image Generation
...Project pix2pix provides a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene, these can be pairs {HE, IHC}. Then we can learn to translate A(HE images) to B(IHC images). The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. ... -
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Blend_My_NFTs
Easily generate thousands of 3D models, images, and animation NFTs
Blend_My_NFTs is an open-source, free-to-use Blender add-on that enables you to easily generate thousands of 3D Models, Animations, and Images. This add-on's primary purpose is to aid in the creation of large generative 3D NFT collections. It is the first and easiest 3D NFT generator. Blend_My_NFTs was initially developed to create Cozy Place, an NFT collection by This Cozy Studio Inc. Blend_My_NFTs works with Blender 3.2.2 on Windows 10 or macOS Big Sur 11.6. Linux is supported, however we... -
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DeepLabv3 Plus
Encoder-Decoder with Atrous Separable Convolution
...The project also supports multi-GPU training, multiple backbones, learning rate schedules with step and cosine options, optimizer selection, and adaptive learning rate behavior based on batch size. It is useful for users who want a stronger semantic segmentation baseline than U-Net for scene-level segmentation tasks.
