Closing the Loop: Training-Free Revisit Consistency for Autoregressive Generative Rendering

  • 类型:arxiv
  • 标识:2607.21848
  • 链接:https://arxiv.org/abs/2607.21848
  • 主分类:multimodal
  • 形态:application
  • TLDR:Recent conditional video generation models have shown promising potentials to transform 3D engine renderings, such as depth maps and untextured geometry, into photorealistic videos for gaming and immersive content creation. These applications require long-horizon auto-regressive generation that continuously synthesizes new frames while preserving a persistent 3D world. Auto-regressive generators synthesize video chunk by chunk with a bounded KV cache, so when the camera revisits a location after its context has been evicted, the model often regenerates inconsistent appearance, even though the
  • 副分类:llm-infra
  • 待LLM分类:否
  • 标题中文:Closing the Loop:面向自回归生成式渲染的无训练 Revisit 一致性
  • TLDR中文:近期条件视频生成模型已展现出将 3D 引擎渲染(如深度图与无纹理几何体)转化为照片级真实视频的潜力,可应用于游戏与沉浸式内容创作。此类应用要求长时程自回归生成,在持续合成新帧的同时维持持久的 3D 世界。自回归生成器以有界 KV cache 逐 chunk 合成视频,因此当相机再次访问已从上下文中驱逐的位置时,模型常会重新生成不一致的外观,尽管该
  • 来源文件
  • /inbox/tom/_candidates/2026-07-27-agent-rag-longcontext-candidates.json