Robust Motion Generation using Part-level Reliable Data from Videos
Quick summary
arXiv:2512.12703v2 Announce Type: replace-cross Abstract: Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation. However, some human parts in many video frames cannot be seen due to off-screen captures or occlusions. It brings a dilemma: discarding the data missing any part limits scale and diversity, while retaining it compromises data quality and model performance. To address this problem, we propose leveraging credible part-level data extracted from videos to enhance motion generation via a robust part-aware masked a
Key takeaways
- arXiv:2512.12703v2 Announce Type: replace-cross Abstract: Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation.
- However, some human parts in many video frames cannot be seen due to off-screen captures or occlusions.
- It brings a dilemma: discarding the data missing any part limits scale and diversity, while retaining it compromises data quality and model performance.
Why it matters
“Robust Motion Generation using Part-level Reliable Data from Videos” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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