The paper surveys recent progress in video foundation and world models, framing both as ways to relate video observations to an evolving latent world. It categorizes the literature into four operations—reading, writing, sharing, and interacting—highlighting how each addresses different aspects of understanding, generating, integrating, and updating world states from video. The authors trace a shift from clip‑level recognition toward persistent, controllable, and physically valid world representations, and outline remaining challenges.
arXiv:2610.19063v1 Announce Type: new Abstract: As video models enable longer and more realistic interactive experiences, maintaining a world consistent with its history becomes essential. This requi...
arXiv:2605.15618v2 Announce Type: replace-cross Abstract: Self-supervised video models are increasingly framed as world models, yet they are still evaluated almost entirely on clean video and reporte...
arXiv:2606. 10620v1 Announce Type: cross Abstract: Image generation models now produce high-quality static images, yet their ability to represent how a visual world changes over time remains poorly understood.
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.
StateCraft introduces a new architecture that separates a persistent, updatable abstract state from the visual state in video world models. By pairing a language model with a video diffusion model, it tracks entity changes over time and conditions visual generation on this abstract state. The approach is evaluated on generative interfaces and a new benchmark, showing improved generation quality, memory usage, and controllability, while enabling direct editing of unseen states and extending to spatial 3D environments.
arXiv:2511.17481v2 Announce Type: replace Abstract: World models learn to predict the temporal evolution of visual observations given a control signal, potentially enabling agents to reason about env...
EvoWorld introduces a framework that couples a vision‑language model with a segment‑autoregressive video renderer to enable generative video models to reason about world evolution beyond visual fidelity. The system maintains an evolving account of the world, infers event consequences, and renders causally plausible outcomes even when events occur out of sight. The accompanying EvoWorld‑Data and EvoWorld‑Bench datasets provide training and evaluation for event realization, causal reactions, and revisit consistency.
arXiv:2609.28466v1 Announce Type: new Abstract: Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environme...
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.
Oneira is an interactive video world model that links open-ended generation with persistent interaction. It uses a coding agent to manage an extensible world state, allowing newly generated or discovered entities to become actionable and ensuring that changes made by an agent are retained across video segments. Experiments demonstrate that Oneira supports consistent interaction with new objects while preserving prior interaction effects over long horizons.
arXiv:2607. 26452v1 Announce Type: new Abstract: World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually capture only part of this structure.
arXiv:2610.33169v1 Announce Type: new Abstract: Interactive video world models require coordinated control over viewing perspectives, character actions, and camera motion. Existing methods typically...