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

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/10/2026, 09:18:25 UTC.

Briefings

CurveCodec 2 reduces skeletal animation compression by predicting quantized curves from past history and using a learned model to encode residuals as bit-exact integer values. It achieves 37% fewer bytes at production precision compared to ACL, with no retraining required for new datasets.

Key points

• CurveCodec 2 achieves 37% fewer bytes than ACL at production precision by predicting quantized curves from their own past history.
• The learned model encodes residuals as bit-exact integer values across platforms without requiring retraining on new datasets.

Context

This research addresses skeletal motion compression in game engines by analyzing redundancy in joint transforms. It compares CurveCodec 2 against ACL, the production library of modern game engines, to determine optimal compression strategies.

Limitations

The study uses a held-out test set of 4,472 clips from 33 datasets; no additional background context is provided beyond the evidence. Results are based on measurements and do not include forecasts or causal claims beyond what the data supports.

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