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This is a Quantitative Study study (also classified as: Experimental Study).

October 2, 2025Open Access

Real-time and Controllable Reactive Motion Synthesis via Intention Guidance

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This method enhances pose prediction stability in interactive environments by using intention prediction and adversarial training.

This approach enables real-time motion generation, outperforming previous methods with superior stability and generalizability.
An intention predictor helps forecast key joint intentions, enhancing deterministic pose prediction from historical interactions.
The synthesis method utilizes adversarial training to improve the robustness of reactive motion outputs.
Users can actively influence outcomes by controlling moving directions, leading to personalized interaction paths.

Zhang et al. (2025) studied this question.

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October 2, 2025Open Access

View Full Paper

Ask AI

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Authors

This method enhances pose prediction stability in interactive environments by using intention prediction and adversarial training.

This approach enables real-time motion generation, outperforming previous methods with superior stability and generalizability.
An intention predictor helps forecast key joint intentions, enhancing deterministic pose prediction from historical interactions.
The synthesis method utilizes adversarial training to improve the robustness of reactive motion outputs.
Users can actively influence outcomes by controlling moving directions, leading to personalized interaction paths.

Zhang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark