Synthetic-to-Real Translation for Class-Agnostic Motion Prediction
Motion understanding is critical for ensuring safety and robustness in autonomous driving systems, driving increasing interest in motion prediction. A key challenge in this domain is the high cost associated with acquiring real-world motion labels. It is therefore ideal if we could transfer motion knowledge from synthetic data to real data. In this context, we explore the potential of synthetic-to-real translation for motion prediction (SRMP). However, the most used naive motion regression methods are notably sensitive to the synthetic-to-real domain shift, resulting in unreliable knowledge tr
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 47%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- PossiblePossibly related (embedding) · 46%From virtual experiments to biomedical insight with synthetic data →
- LinkedLinked via arxiv author · 85%Yizheng Wu →
“Synthetic-to-Real Translation for Class-Agnostic Motion Prediction”
- LinkedLinked via arxiv author · 85%Hongwei Fan →
“Synthetic-to-Real Translation for Class-Agnostic Motion Prediction”
- LinkedLinked via arxiv author · 85%Kewei Wang →
“Synthetic-to-Real Translation for Class-Agnostic Motion Prediction”
- LinkedLinked via arxiv author · 85%Ruibo Li →
“Synthetic-to-Real Translation for Class-Agnostic Motion Prediction”
- LinkedLinked via arxiv author · 85%Xingyi Li →
“Synthetic-to-Real Translation for Class-Agnostic Motion Prediction”
- LinkedLinked via arxiv author · 85%Xiao Song →
“Synthetic-to-Real Translation for Class-Agnostic Motion Prediction”