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The core idea of CoupledFlow is to take the joint state of multiple physical processes as conditional input and learn the respective mean velocity field of each process.
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11/10/2026
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UnverifiedCoupledFlow adopts a structure-constrained flow matching algorithm; unlike step-by-step iterative diffusion-style generation, flow matching achieves fast sampling by learning a velocity field, thereby enabling one-step prediction.72% similarUnverifiedThe technical principle behind four-step generation typically comes from accelerated sampling paradigms such as Consistency Models or Flow Matching, which use distillation training to enable the model to accurately predict the denoising direction at sparse time steps.71% similarUnverifiedRectified flow is a class of generative models based on ordinary differential equations that learns a velocity field to transport a simple distribution along approximately straight trajectories to the data distribution, and can complete generation with fewer sampling steps than diffusion models.69% similarUnverifiedFlowMoP proposes a conditional flow matching framework that jointly generates diverse future motions for multiple agents while modeling global trajectories and root-relative poses.68% similarUnverifiedDeterministic physics simulation means that the physics engine produces completely identical output results every time given the same input conditions.68% similar
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