awesome-repositories.com
博客
MCP
awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索开源替代品自托管软件博客网站地图
项目MCP 服务器关于排名机制媒体报道
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 个仓库

Awesome GitHub RepositoriesBatch Frame Processing Pipelines

Processes multiple video frames simultaneously in batches to maximize GPU throughput and reduce per-frame overhead.

Distinct from Batch Video Processing: Distinct from Batch Video Processing: focuses on the pipeline architecture for frame-level batching, not general multi-file workflows.

Explore 2 awesome GitHub repositories matching graphics & multimedia · Batch Frame Processing Pipelines. Refine with filters or upvote what's useful.

Awesome Batch Frame Processing Pipelines GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • justin62628/squirrel-rifeJustin62628 的头像

    Justin62628/Squirrel-RIFE

    3,471在 GitHub 上查看↗

    Squirrel-RIFE is a GPU-accelerated video processing tool that uses a neural network to generate intermediate frames between existing video frames, enabling smooth slow-motion effects and frame rate conversion. It is built around the RIFE (Real-Time Intermediate Flow Estimation) model, which analyzes motion between consecutive frames to predict and insert new frames, and leverages NVIDIA CUDA for parallel processing to achieve high-speed inference. The tool distinguishes itself by combining neural frame interpolation with practical video preprocessing features, including pixel-level duplicate

    Processes multiple video frames simultaneously in batches to maximize GPU throughput and reduce per-frame overhead.

    Pythonanimationinterpolationrife
    在 GitHub 上查看↗3,471
  • tensorboy/pytorch_realtime_multi-person_pose_estimationtensorboy 的头像

    tensorboy/pytorch_Realtime_Multi-Person_Pose_Estimation

    1,372在 GitHub 上查看↗

    This project is a deep learning framework built for detecting and tracking human body keypoints in images and video streams. It functions as both a real-time motion tracking system and a machine learning environment for training and evaluating pose estimation models. The system utilizes a two-branch convolutional neural network to predict body part locations and their directional connections simultaneously. It employs multi-stage feature refinement to improve keypoint localization accuracy and uses greedy parsing and bipartite matching algorithms to associate detected parts into individual sk

    Executes parallel tensor-based batch processing to maintain high frame rates during real-time video analysis.

    Python
    在 GitHub 上查看↗1,372
  1. Home
  2. Graphics & Multimedia
  3. Batch Video Processing
  4. Batch Frame Processing Pipelines