World-in-Context: Interactive Video World Modeling with In-Context Conditioning
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
World in World introduces a training‑free, inference‑time interface that transforms diverse control signals—such as source‑video observations, target‑view projections, geometry renderings, and retrieved states—into camera‑ and time‑labelled visual states. These states are processed by a frozen causal video model’s self‑attention, enabling tasks like camera‑controlled rerendering, long‑horizon revisiting, and human‑motion transfer without additional training. The method employs a correspondence router and evidence‑wise attention to align token identities and regulate auxiliary channel contributions during a single denoising pass.
World in World introduces a training‑free inference interface that lets users control autoregressive video world models from new viewpoints. By converting diverse control signals—source‑video observations, target‑view projections, geometry renderings, and retrieved states—into camera‑ and time‑labelled visual tokens, the system uses a frozen causal video model’s self‑attention to maintain synchronization, complete unseen regions, and recover past appearances. The method supports camera‑controlled rerendering, long‑horizon revisiting, and human‑motion transfer while preserving perceptual quality, temporal consistency, and camera‑following accuracy.
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