MIC casts diffusion motion generation as stochastic control to support both objective-based and criterion-based constraints without training or differentiability requirements.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
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PoseShield learns a neural collision field directly in SMPL pose space with Eikonal regularization to correct self-collisions post-hoc in human pose estimation and motion generation, achieving 95.8% success on a new benchmark.
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Training-free Controllable Human Motion Generation under Heterogeneous Constraints
MIC casts diffusion motion generation as stochastic control to support both objective-based and criterion-based constraints without training or differentiability requirements.
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PoseShield: Neural Collision Fields for Human Self-Collision Resolution
PoseShield learns a neural collision field directly in SMPL pose space with Eikonal regularization to correct self-collisions post-hoc in human pose estimation and motion generation, achieving 95.8% success on a new benchmark.