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CoViST is proposed, a training-free framework that represents a compressed image as a composable visual state that retains its interpretation under successive reductions, enabling the same formulation to support both fixed compression before prefill and progressive compression within the decoder.
This paper proposes DualDiff, a novel pipeline that leverages dual diffusion priors with a Structure-Appearance Attention (SAA) module to introduce reference guidance for refining low-quality novel views rendered from flawed 3D representations, which outperforms state-of-the-art methods even in the inference-only setti...
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