Unverified50% confidenceFactExact time
CoTL (Compact Token Learning) proposes using a small set of compact latent Tokens to preserve the geometric and appearance information of a scene, allowing representation capacity to scale naturally with the number of observations.
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10/14/2026
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UnverifiedThe paper conducted experiments on datasets such as ACID, and the results show that CoTL's compact, unstructured Token set not only supports competitive novel-view synthesis but can also serve as a latent space for generative models to generate 3D Gaussian scenes.73% similarUnverifiedIn CoTL's encoder, each additional view only requires extending the representation by a small portion of resolution-independent Tokens, allowing the representation's capacity to grow flexibly with the number of available observations rather than being limited by fixed-size network parameters.73% similarUnverifiedVisual Chain-of-Thought (Visual CoT) has the model generate intermediate reasoning images while producing intermediate text steps, such as annotating key regions, predicting future frames, or drawing spatial relationship diagrams.68% similarUnverifiedThe key difference between CoT and recursive models lies in the scope of information visibility: CoT reasoning steps can see each other, while each subtask in a recursive model can only see its own local input.66% similarUnverified扩散模型在训练过程中学习到的是视觉概念的分布式表征，不同IP的视觉元素在潜在空间中以可组合的形式存在63% similar
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