English(EN) CurveCodec 2: Skeleton-agnostic animation compression with a learned entropy model

CurveCodec 2 使用学习熵模型实现 100 倍动画压缩

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 00:00

研究人员开发了 CurveCodec 2，这是一种用于压缩骨骼动画数据的先进模型。这种新编解码器通过学习运动残差的熵模型，显著减小了文件大小，在某些精度级别上，文件大小比标准的 float32 表示小 100 倍。CurveCodec 2 被设计为与骨架无关，这意味着单个模型可以在不重新训练的情况下压缩各种骨架甚至不同物种的动画，展示了其泛化能力。 AI

影响该模型先进的压缩技术可以显著降低 3D 动画资产的存储和传输成本。

排序理由该项目描述了在研究论文中提出的一种用于动画压缩的新型学习熵模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-03 00:00

CurveCodec 2: 骨骼无关的动画压缩与学习熵模型

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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报道来源 [1]

CurveCodec 2: 骨骼无关的动画压缩与学习熵模型

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