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MediaPipe: A Framework for Building Perception Pipelines

28 Pith papers cite this work. Polarity classification is still indexing.

28 Pith papers citing it
abstract

Building applications that perceive the world around them is challenging. A developer needs to (a) select and develop corresponding machine learning algorithms and models, (b) build a series of prototypes and demos, (c) balance resource consumption against the quality of the solutions, and finally (d) identify and mitigate problematic cases. The MediaPipe framework addresses all of these challenges. A developer can use MediaPipe to build prototypes by combining existing perception components, to advance them to polished cross-platform applications and measure system performance and resource consumption on target platforms. We show that these features enable a developer to focus on the algorithm or model development and use MediaPipe as an environment for iteratively improving their application with results reproducible across different devices and platforms. MediaPipe will be open-sourced at https://github.com/google/mediapipe.

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2026 27 2024 1

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representative citing papers

D-Rex : Diffusion Rendering for Relightable Expressive Avatars

cs.GR · 2026-04-30 · conditional · novelty 7.0

D-Rex applies a LoRA-fine-tuned video diffusion model as an image-space post-process to add consistent relighting to any expressive full-body avatar pipeline while preserving motion and facial detail.

Face Anything: 4D Face Reconstruction from Any Image Sequence

cs.CV · 2026-04-21 · unverdicted · novelty 7.0

A single transformer model jointly predicts depth and normalized canonical coordinates to deliver state-of-the-art 4D facial geometry and tracking with 3x lower correspondence error and 16% better depth accuracy.

AvatarPointillist: AutoRegressive 4D Gaussian Avatarization

cs.CV · 2026-04-06 · unverdicted · novelty 7.0

AvatarPointillist autoregressively generates adaptive 3D point clouds via Transformer for photorealistic 4D Gaussian avatars from one image, jointly predicting animation bindings and using a conditioned Gaussian decoder.

Sentiment Analysis of German Sign Language Fairy Tales

cs.CL · 2026-04-17 · unverdicted · novelty 5.0

A new dataset and XGBoost model predict sentiment in German Sign Language fairy tale videos from motion features at 0.631 balanced accuracy, showing body movements contribute equally to facial ones.

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Showing 28 of 28 citing papers.