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cs.CV2025
GrndCtrl: Grounding World Models via Self-Supervised Reward Alignment
Haoyang He, Jay Patrikar, Dong-Ki Kim +5
Recent advances in video world modeling have enabled large-scale generative models to simulate embodied environments with high visual fidelity, providing strong priors for predicti…
cs.CV2025
Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers
Yutian Chen, Yuheng Qiu, Ruogu Li +4
We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the base model. Co-Me distilled a l…