Paper Overview
Research area: Computer Vision (CV) Authors: Kiymet Akdemir, Pinar Yanardag Published: 2026-06-03 arXiv: 2506.00005
Summary
Autoregressive world models have emerged as a powerful paradigm for interactive video generation, allowing users to navigate dynamically generated environments through actions. These models are typically conditioned on a text prompt and/or a single reference frame, from which the entire world is generated. However, once the user navigates beyond what is visible in that frame, unseen regions are populated by the base model's priors, with no mechanism for users to specify what should appear and where. This is a fundamental limitation for applications such as gaming, interactive storytelling, and simulation, where controllable scene composition is essential.
The authors refer to this missing capability as concept spawning: introducing a user-specified visual concept into a world model, analogous to spawning in game engines.
Method: SPAWN
SPAWN (Swapping fixed Anchors via Windows INjection) is a training-free concept spawning method. It exploits a structural property of the image-to-video backbone:
- The first slot of the model's context memory is fixed to the reference frame and serves as the foundational anchor for every generation block.
- By swapping this anchor with an external concept latent over a short injection window, then letting the original anchor return, the concept naturally propagates through the model's own memory across the entire generation process.
- Supports concepts ranging from fine-grained entities (characters, props) to large-scale elements (buildings, landmarks).
- Accepts either concept images or text descriptions as input.
- Integrates concepts with consistent lighting, scale, and viewpoint.
- Preserves identity and temporal coherence, all without any training on existing autoregressive world models.
- arXiv: https://arxiv.org/abs/2506.00005
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