• DensePose From WiFi using ML

    https://arxiv.org/pdf/2301.00250

    https://www.xrstager.com/en/ai-based-motion-detection-without-cameras-using-wifi

    Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation using RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by common issues such as occlusion and lighting, which can significantly hinder performance in various scenarios.

    Radar and LiDAR technologies, while useful, require specialized hardware that is both expensive and power-intensive. Moreover, deploying these sensors in non-public areas raises important privacy concerns, further limiting their practical applications.

    To overcome these limitations, recent research has explored the use of WiFi antennas, which are one-dimensional sensors, for tasks like body segmentation and key-point body detection. Building on this idea, the current study expands the use of WiFi signals in combination with deep learning architectures—techniques typically used in computer vision—to estimate dense human pose correspondence.

    In this work, a deep neural network was developed to map the phase and amplitude of WiFi signals to UV coordinates across 24 human regions. The results demonstrate that the model is capable of estimating the dense pose of multiple subjects with performance comparable to traditional image-based approaches, despite relying solely on WiFi signals. This breakthrough paves the way for developing low-cost, widely accessible, and privacy-preserving algorithms for human sensing.

  • VFX pipeline – Render Wall management topics

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    1: Introduction Title: Managing a VFX Facility’s Render Wall

    • Briefly introduce the importance of managing a VFX facility’s render wall.
    • Highlight how efficient management contributes to project timelines and overall productivity.

     

    2: Daily Overview Title: Daily Management Routine

    • Monitor Queues: Begin each day by reviewing render queues to assess workload and priorities.
    • Resource Allocation: Allocate resources based on project demands and available hardware.
    • Job Prioritization: Set rendering priorities according to project deadlines and importance.
    • Queue Optimization: Adjust queue settings to maximize rendering efficiency.

     

    3: Resource Allocation Title: Efficient Resource Management

    • Hardware Utilization: Distribute rendering tasks across available machines for optimal resource usage.
    • Balance Workloads: Avoid overloading specific machines while others remain underutilized.
    • Consider Off-Peak Times: Schedule resource-intensive tasks during off-peak hours to enhance overall performance.

     

    4: Job Prioritization Title: Prioritizing Rendering Tasks

    • Deadline Sensitivity: Give higher priority to tasks with imminent deadlines to ensure timely delivery.
    • Critical Shots: Identify shots crucial to the project’s narrative or visual impact for prioritization.
    • Dependent Shots: Sequence shots that depend on others should be prioritized together.

     

    5: Queue Optimization and Reporting Title: Streamlining Render Queues

    • Dependency Management: Set up dependencies to ensure shots are rendered in the correct order.
    • Error Handling: Implement automated error detection and requeueing mechanisms.
    • Progress Tracking: Regularly monitor rendering progress and update stakeholders.
    • Data Management: Archive completed renders and remove redundant data to free up storage.
    • Reporting: Provide daily reports on rendering status, resource usage, and potential bottlenecks.

     

    6: Conclusion Title: Enhancing VFX Workflow

    • Effective management of a VFX facility’s render wall is essential for project success.
    • Daily monitoring, resource allocation, job prioritization, queue optimization, and reporting are key components.
    • A well-managed render wall ensures efficient production, timely delivery, and overall project success.