Showing 7 open source projects for "code visualization"

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  • 1
    LTX-2

    LTX-2

    Python inference and LoRA trainer package for the LTX-2 audio–video

    ...Beyond basic rendering scaffolding, LTX-2 includes optimized math libraries, resource loaders, utilities for texture and buffer handling, and integration points for native event loops and input systems. The framework targets both interactive graphical applications and media-rich experiences, making it a solid foundation for games, creative tools, or visualization systems that demand both performance and flexibility. While being low-level, it also provides sensible defaults and helper abstractions that reduce boilerplate and help teams maintain clear, maintainable code.
    Downloads: 67 This Week
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  • 2
    DeepSeek-V3.2-Exp

    DeepSeek-V3.2-Exp

    An experimental version of DeepSeek model

    DeepSeek-V3.2-Exp is an experimental release of the DeepSeek model family, intended as a stepping stone toward the next generation architecture. The key innovation in this version is DeepSeek Sparse Attention (DSA), a sparse attention mechanism that aims to optimize training and inference efficiency in long-context settings without degrading output quality. According to the authors, they aligned the training setup of V3.2-Exp with V3.1-Terminus so that benchmark results remain largely...
    Downloads: 29 This Week
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  • 3
    SAM 3D Body

    SAM 3D Body

    Code for running inference with the SAM 3D Body Model 3DB

    SAM 3D Body is a promptable model for single-image full-body 3D human mesh recovery, designed to estimate detailed human pose and shape from just one RGB image. It reconstructs the full body, including feet and hands, using the Momentum Human Rig (MHR), a parametric mesh representation that decouples skeletal structure from surface shape for more accurate and interpretable results. The model is trained to be robust in diverse, in-the-wild conditions, so it handles varied clothing,...
    Downloads: 5 This Week
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  • 4
    Mesh R-CNN

    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. Unlike voxel-based or point-based approaches, Mesh...
    Downloads: 0 This Week
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  • 5
    DiT (Diffusion Transformers)

    DiT (Diffusion Transformers)

    Official PyTorch Implementation of "Scalable Diffusion Models"

    DiT (Diffusion Transformer) is a powerful architecture that applies transformer-based modeling directly to diffusion generative processes for high-quality image synthesis. Unlike CNN-based diffusion models, DiT represents the diffusion process in the latent space and processes image tokens through transformer blocks with learned positional encodings, offering scalability and superior sample quality. The model architecture parallels large language models but for image tokens—each block...
    Downloads: 0 This Week
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  • 6
    Mask2Former

    Mask2Former

    Code release for "Masked-attention Mask Transformer

    Mask2Former is a unified segmentation architecture that handles semantic, instance, and panoptic segmentation with one model and one training recipe. Its core idea is to cast segmentation as mask classification: a transformer decoder predicts a set of mask queries, each with an associated class score, eliminating the need for task-specific heads. A pixel decoder fuses multi-scale features and feeds masked attention in the transformer so each query focuses computation on its current spatial...
    Downloads: 0 This Week
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  • 7
    Multi-Agent Emergence Environments

    Multi-Agent Emergence Environments

    Environment generation code for the paper "Emergent Tool Use"

    multi-agent-emergence-environments is an open source research environment framework developed by OpenAI for the study of emergent behaviors in multi-agent systems. It was designed for the experiments described in the paper and blog post “Emergent Tool Use from Multi-Agent Autocurricula”, which investigated how complex cooperative and competitive behaviors can evolve through self-play. The repository provides environment generation code that builds on the mujoco-worldgen package, enabling...
    Downloads: 0 This Week
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