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MINT is proposed, which first trains VLA policies to remain functional under missing visual inputs, and selectively supplements missing observations using optical-flow extrapolation or an action-conditioned world model, and withdraws predicted views when they become unreliable.
This paper proposes a simple yet effective two-stage training strategy along with a style-strength-aware spline formulation to enable modern Diffusion Transformer-based multi-reference editing models to faithfully preserve the semantic structure of the content image, render strong stylization effects, and smoothly tran...
The proposed MaskFlow, a training framework for precise localization, consistent background preservation, and seamless boundary transitions, incorporates the mask into the probability path and flow-matching objective, coordinating generation within the editable region with source preservation outside it.
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