FlowBender introduces closed-loop training that lets conditional flow models learn correction policies from their own task-specific alignment errors, outperforming supervised and guidance baselines on fidelity and plausibility.
arXiv:2009.11243 (2020) 4
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A meta-learned optimizer for 3DGS that extends the optimization horizon via checkpoint buffers and latent gradient-scale encoding, delivering better early novel-view quality and long-term stability with zero-shot generalization.
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FlowBender: Feedback-Aware Training for Self-Correcting Conditional Flows
FlowBender introduces closed-loop training that lets conditional flow models learn correction policies from their own task-specific alignment errors, outperforming supervised and guidance baselines on fidelity and plausibility.
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Learn2Splat: Extending the Horizon of Learned 3DGS Optimization
A meta-learned optimizer for 3DGS that extends the optimization horizon via checkpoint buffers and latent gradient-scale encoding, delivering better early novel-view quality and long-term stability with zero-shot generalization.