QCObjects

QCObjects recognized as Startup of the Year 2021

Autonomous APIs are the Future - Interview with Jean Machuca, Founder, QCObjects

Here are some recommended settings for a good result doing your first steps creating a PWA using QCObjects:

Best Front-End Settings To Create a PWA [QCObjects Edition]

Shadowed Components & QCObjects

This article and the repo referenced here is intended to help Data Scientists and data processing professionals to handle and manage data related to the COVID-19 disease outbreak. I hope this information helps researchers to find new ways of analysis to defeat the COVID-10 disease outbreak, as well as empower them to solve more complex math calc using less time.

How to Process Pandemics Data from WHO.int

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This is a simplified guide to an AI model called sam3-video [https://www.aimodels.fyi/models/replicate/sam3-video-lucataco?utm_source=hackernoon&utm_medium=referral] maintained by lucataco [https://www.aimodels.fyi/creators/replicate/lucataco?utm_source=hackernoon&utm_medium=referral]. If you like these kinds of analysis, join AIModels.fyi [https://www.aimodels.fyi/?utm_source=hackernoon&utm_medium=referral] or follow us on Twitter [https://x.com/aimodelsfyi].


MODEL OVERVIEW

sam3-video is a unified foundation model from Meta Research designed for prompt-based segmentation in both images and videos. The model accepts text descriptions, visual prompts like points and boxes, or object tracking across frames to identify and isolate specific elements within video content. This approach differs from earlier segmentation tools that often require manual mask creation or frame-by-frame annotation. The creator, lucataco [https://aimodels.fyi/creators/replicate/lucataco?utm_source=hackernoon&utm_medium=referral], has implemented this model on Replicate for accessible video segmentation workflows.

For related work in this space, you may find Segment Anything 2 [https://aimodels.fyi/models/replicate/segment-anything-2-lucataco?utm_source=hackernoon&utm_medium=referral] useful for automatic mask generation, while videollama3-7b [https://aimodels.fyi/models/replicate/videollama3-7b-lucataco?utm_source=hackernoon&utm_medium=referral] provides multimodal video understanding capabilities. When you need to prepare video files for processing, trim-video [https://aimodels.fyi/models/replicate/trim-video-lucataco?utm_source=hackernoon&utm_medium=referral] and video-merge [https://aimodels.fyi/models/replicate/video-merge-lucataco?utm_source=hackernoon&utm_medium=referral] offer quick editing utilities.


MODEL INPUTS AND OUTPUTS

The model takes video files and text prompts as primary inputs, then outputs segmented video with customizable visualization options. You can control mask appearance through color selection and opacity settings, and request either overlay videos or raw PNG mask sequences for further editing.


INPUTS

 * video: The input video file to process (required)
 * prompt: Text description of the object to segment, such as "person", "car", or "foot" (required)
 * negative_prompt: Optional text to specify objects to exclude from segmentation
 * visual_prompt: Optional JSON string for advanced point-based or box-based prompts
 * mask_color: Color of the segmentation overlay (green, red, blue, yellow, cyan, or magenta; default is green)
 * mask_opacity: Opacity level from 0.0 to 1.0 (default 0.5)
 * mask_only: Boolean to return high-contrast black and white masks instead of overlays
 * return_zip: Boolean to request a ZIP file containing individual PNG masks for every frame


OUTPUTS

 * Output: A video file with segmentation overlay or a ZIP archive containing the overlay video and frame-by-frame PNG masks


CAPABILITIES

The model segments objects across video frames using natural language descriptions. You can specify "foot" to isolate footwear in sports footage, request "person" to track individuals through scenes, or use color and opacity controls to create specific visual styles. The model handles multi-frame consistency, maintaining object identification across temporal sequences. You can also extract raw binary masks for compositing in professional video editing software, enabling integration with After Effects or Premiere Pro workflows.


WHAT CAN I USE IT FOR?

Video content creators can use this for automated object isolation in post-production, reducing manual rotoscoping hours significantly. Marketing teams can generate product highlights by segmenting specific items in demonstration videos. Film and television productions benefit from efficient tracking-based masking for visual effects compositing. Educational content creators can isolate anatomical structures or mechanical components in instructional videos. You can also license segmentation services to clients needing automated video processing, leveraging the text-prompt interface to offer accessible tools without requiring technical masking knowledge.


THINGS TO TRY

Experiment with negative prompts to exclude similar objects—for example, prompt "person" with negative prompt "face" to segment only body portions. Generate multiple mask extractions with different opacity levels in a single batch, then composite them as layers for advanced color grading effects. Test visual prompts by providing point coordinates alongside text descriptions to refine segmentation boundaries when text alone produces imprecise results. Extract raw PNG masks from full videos, then use them to create custom color grades or selective blur effects in your editing software rather than relying on the built-in overlay colors.

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Original post: Read on AIModels.fyi [https://www.aimodels.fyi/models/replicate/sam3-video-lucataco?utm_source=hackernoon&utm_medium=referral]

QCOBJECTS

qcobjects.com

Since n.d.

QCObjects's stories on HackerNoon

Autonomous APIs are the Future - Interview with Jean Machuca, Founder, QCObjects

Best Front-End Settings To Create a PWA [QCObjects Edition]

Shadowed Components & QCObjects

How to Process Pandemics Data from WHO.int

How A Full Stack Framework Can Make An Impact In Your Business

213 Stories To Learn About Writing Prompts

209 Stories To Learn About Website Development

277 Stories To Learn About Microservices

129 Stories To Learn About Javascript Frameworks

338 Stories To Learn About Frontend

65 Stories To Learn About Framework

Meet the Winners of Startups in South America

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Segment Anything in Motion: A Hands-On Guide to sam3-video

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