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CurveCodec 2 achieves 100x animation compression with learned entropy model

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CurveCodec 2, an advanced model for compressing skeletal animation data. This new codec significantly reduces file sizes by learning an entropy model for motion residuals, achieving up to 100x smaller file sizes compared to standard float32 representations at certain precision levels. CurveCodec 2 is designed to be skeleton-agnostic, meaning a single model can compress animations for various skeletons and even different species without retraining, demonstrating its generalization capabilities. AI

IMPACT This model's advanced compression techniques could significantly reduce storage and transmission costs for 3D animation assets.

RANK_REASON The item describes a new learned entropy model for animation compression presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CurveCodec 2 achieves 100x animation compression with learned entropy model

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The item describes a new learned entropy model for animation compression presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    CurveCodec 2: Skeleton-agnostic animation compression with a learned entropy model

    Skeletal motion is stored as every joint's transform at every frame, yet most of it is implied by the body rather than by what the motion is about. Compression is one way to ask what a motion must still say once the body is known, and a production codec must answer it for any ske…

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