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Showing 11 open source projects for "view"
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1
CO3D (Common Objects in 3D)
Tooling for the Common Objects In 3D dataset
...It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. CO3Dv2 enables research in multi-view 3D reconstruction, novel view synthesis, and geometry-aware representation learning. Each of the thousands of sequences in CO3Dv2 captures a common object (from categories like cars, chairs, or plants) from multiple real-world viewpoints. The dataset includes RGB images, depth maps, masks, and camera poses for each frame, along with pre-defined training, validation, and testing splits for both few-view and many-view reconstruction tasks. -
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HY-World 2.0
A Multi-Modal World Model for Reconstructing, Generating, Simulation
...The system also improves reconstruction from multi-view images and video by upgrading its feed-forward 3D prediction components and its memory-aware view generation process. Another major part of the project is WorldLens, a rendering platform designed for interactive exploration with an engine-agnostic architecture, automatic image-based lighting, collision detection, and support for character interaction. -
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Stable Virtual Camera
Stable Virtual Camera: Generative View Synthesis with Diffusion Models
Stable Virtual Camera is a multi-view diffusion model developed by Stability AI that transforms 2D images into immersive 3D videos with realistic depth and perspective. Unlike traditional methods that require complex reconstruction or scene-specific optimization, this model allows users to generate novel views from any number of input images and define custom camera trajectories, enabling dynamic exploration of scenes. -
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Hunyuan3D 2.0
High-Resolution 3D Assets Generation with Large Scale Diffusion Models
The Hunyuan3D-2 model, developed by Tencent, is designed for generating high-resolution 3D assets using large-scale diffusion models. This model offers advanced capabilities for creating detailed 3D models, including texture enhancements, multi-view shape generation, and rapid inference for real-time applications. It is particularly useful for industries requiring high-quality 3D content, such as gaming, film, and virtual reality. Hunyuan3D-2 supports various enhancements and is available for deployment through tools like Blender and Hugging Face. Includes a user-friendly production/studio tool (Hunyuan3D-Studio) to manipulate/animate meshes. ... -
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Step1X-3D
High-Fidelity and Controllable Generation of Textured 3D Assets
...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 texture module. The result is fully 3D assets — meshes + textures — which can be rendered from any viewpoint, textured consistently, and used in 3D applications. To achieve this, the project includes a massive curated dataset: among more than 5 million candidate 3D assets, it filters and standardizes to produce a high-quality 2 million–asset subset suitable for training. -
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Seamless Communication
Foundational Models for State-of-the-Art Speech and Text Translation
...The system architecture includes a real-time multimodal signal pipeline for audio, video, and sensor data, a dialog manager that can decide when to act (speak, gesture, point) or query, and a cross-modal reasoning layer that fuses perception with semantic context. The research prototype includes components for visual grounding (understanding when a user references something in view), gesture recognition and synthesis, and turn-taking mechanisms that mirror human conversational timing. Because latency and synchronization are critical, the codebase invests in asynchronous scheduling, overlap of perception and reasoning, and fast fallback responses. -
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Map-Anything
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
...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, sparse or dense depth) and produces a rich set of outputs including per-pixel 3D points, camera intrinsics, camera poses, ray directions, confidence maps, and validity masks. ... -
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Qwen-Image
Qwen-Image is a powerful image generation foundation model
...Qwen-Image supports sophisticated editing tasks such as style transfer, object insertion and removal, detail enhancement, and even human pose manipulation, making it suitable for both professional and casual users. It also includes advanced image understanding capabilities like object detection, semantic segmentation, depth and edge estimation, and novel view synthesis. -
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HunyuanWorld-Mirror
Fast and Universal 3D reconstruction model for versatile tasks
HunyuanWorld-Mirror focuses on fast, universal 3D reconstruction that can ingest varied inputs and produce multiple kinds of 3D outputs. The model accepts combinations of images, camera intrinsics and poses, or even depth cues, then reconstructs consistent 3D geometry suitable for downstream rendering or editing. The pipeline emphasizes both speed and flexibility so creators can go from casual captures to assets without elaborate capture rigs. Outputs can include point clouds, estimated... -
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LaMDA-pytorch
Open-source pre-training implementation of Google's LaMDA in PyTorch
...This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model. -
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Qwen-Image-Edit
An advanced bilingual image editing with semantic control
...It leverages both Qwen2.5-VL for semantic control and a VAE Encoder for appearance control, enabling users to edit at both the content and detail level. The model excels at semantic edits like style transfer, object rotation, and novel view synthesis, while also handling precise appearance edits such as adding or removing elements without altering surrounding regions. A standout feature is its bilingual text editing in English and Chinese, which preserves original font, size, and style during modifications. Benchmarks confirm its state-of-the-art performance in image editing, establishing it as a reliable foundation for both artistic and practical tasks. ...
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