Synthetic-to-Real Translation for Class-Agnostic Motion Prediction

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The SR-Motion framework addresses the challenge of synthetic-to-real translation for class-agnostic motion prediction (SRMP) in autonomous driving, aiming to overcome the high cost of real-world motion labels and domain shift sensitivity. Traditional methods suffer significant performance degradation, with models trained solely on synthetic data showing a 150.5% error increase and naive teacher-student frameworks worsening it by 202.7% due to noisy pseudo-labels. SR-Motion introduces two key components: Objectness-aware motion prediction (OAMNet), which learns domain-invariant features by modeling motion patterns and objectness priors, and Objectness-aided motion enhancement (OAME), which refines motion labels by filtering noise using these priors. Additionally, the paper presents Motion4D, the first synthetic 4D LiDAR dataset for SRMP, featuring 1,370 sequences and 124K frames. Experimental results demonstrate SR-Motion's effectiveness, reducing mean error for fast-moving agents from 2.702 to 1.545 on Waymo and narrowing the static group's performance gap by 76.3%.

Key takeaway

For autonomous driving engineers developing motion prediction systems, especially with limited real-world labeled data, directly applying models trained on synthetic data or using naive domain adaptation is insufficient. You should integrate objectness-aware prediction and robust pseudo-label enhancement, like SR-Motion's OAMNet and OAME modules, to bridge synthetic-to-real domain gaps effectively. Leverage high-fidelity synthetic datasets such as Motion4D to reduce annotation costs and achieve performance comparable to fully-supervised methods.

Key insights

Synthetic-to-real motion prediction for autonomous driving is significantly improved by integrating objectness priors and a specialized 4D LiDAR dataset.

Principles

• Objectness priors mitigate motion regression sensitivity to domain shifts.
• Teacher-student frameworks require robust pseudo-label refinement for dense regression.
• Physically-based simulation pipelines generate high-fidelity 4D LiDAR datasets.

Method

SR-Motion employs a teacher-student framework where an Objectness-Aware Motion Prediction network (OAMNet) generates initial pseudo-labels. These are refined by an Objectness-Aided Motion Enhancement (OAME) module using clustering, outlier filtering, and spatial smoothing.

In practice

• Integrate objectness-aware branches into motion prediction networks.
• Employ dual-path consistency for pseudo-label validation.
• Utilize EMA for stable teacher model updates.

Topics

• Synthetic-to-Real Translation
• Motion Prediction
• Autonomous Driving
• LiDAR Data
• Objectness Priors
• Domain Adaptation
• Motion4D Dataset

Code references

• ashawkey/cubvh

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

Related on AIssential

• FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model — FrozenDrive generates consistent multi-view driving scenes and augments data using a parameter-free frozen diffusion model.
• Ego-Human Motion Prediction with 3D-Aware LLM — Accurate egocentric motion prediction requires holistic 3D spatial-semantic understanding and integrated pose-language decoding.
• Real2SAM2Real: Generative 3D Caches as Complementary Context for Video Diffusion — Real2SAM2Real uses editable 3D caches from lifting models to provide robust geometric scaffolds for Video Diffusion Models, improving control and consistency.
• Diffusion Transformer World-Action Model for AV Scene Prediction — Diffusion Transformers predict realistic AV future scenes, outperforming regression on perception metrics despite distortion metric bias.
• Driving Safer AVs Faster with Smart Simulation, Neural Reconstruction, and Data-Centric Tools - Ep. 289 — Generative AI and neural reconstruction are accelerating AV simulation, enabling safer, more efficient autonomous driving development.
• A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving — LiDAR super-resolution uses deep learning to enhance sparse point clouds, enabling cheaper sensors to perform like high-resolution ones.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.