CiT achieves SOTA conditional trajectory prediction by cross-time-domain intention interaction that corrects representations using complementary social information from different time domains.
InProceedings of the IEEE conference on computer vision and pattern recognition
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TIGFlow-GRPO uses a Trajectory-Interaction-Graph in conditional flow matching plus Flow-GRPO optimization to produce more accurate, socially compliant, and physically feasible trajectory forecasts on ETH/UCY and SDD datasets.
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Chatting about Conditional Trajectory Prediction
CiT achieves SOTA conditional trajectory prediction by cross-time-domain intention interaction that corrects representations using complementary social information from different time domains.
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TIGFlow-GRPO: Trajectory Forecasting via Interaction-Aware Flow Matching and Reward-Guided Optimization
TIGFlow-GRPO uses a Trajectory-Interaction-Graph in conditional flow matching plus Flow-GRPO optimization to produce more accurate, socially compliant, and physically feasible trajectory forecasts on ETH/UCY and SDD datasets.