MORPHOS: 基于时间结构化潜变量的自回归4D生成
MORPHOS: Autoregressive 4D Generation with Temporal Structured Latents
AI总结：
提出MORPHOS框架，利用时间结构化潜变量（T-SLAT）统一表示4D动态资产，通过自回归因果注意力生成，解决多表示兼容、拓扑变化和长时间一致性问题。
摘要：
我们提出MORPHOS，一种新颖的自回归框架，能够从视频生成动态3D资产，支持多种表示，包括网格、3D高斯和辐射场。现有方法通常局限于单一表示，难以建模拓扑变化，或在长视频中无法保持时间一致性。为解决这些限制，我们引入时间结构化潜变量（T-SLAT），一种统一的4D表示，沿时间维度联合编码几何和外观。利用T-SLAT，MORPHOS通过因果注意力自回归生成动态3D资产，将每一帧条件于其先前历史，以确保时间一致性并处理演化的拓扑。我们还提出一种时间结构增强，以减轻自回归生成中的误差累积。MORPHOS在多个基准测试中实现了外观方面的最先进性能和几何方面的竞争性结果，展示了跨多种表示的卓越泛化能力和长时程生成的鲁棒性。
英文摘要：
We present MORPHOS, a novel autoregressive framework that generates dynamic 3D assets from videos across diverse representations, including meshes, 3D Gaussians, and radiance fields. Existing methods are typically limited to a single representation, struggle to model topological changes, or fail to maintain temporal consistency over long videos. To address these limitations, we introduce the Temporal Structured Latents (T-SLAT), a unified 4D representation that jointly encodes geometry and appearance along the temporal dimension. Leveraging T-SLAT, MORPHOS autoregressively generates dynamic 3D assets via causal attention, conditioning each frame on its preceding history to ensure temporal consistency while handling evolving topologies. We also propose a temporal-structural augmentation to mitigate error accumulation in autoregressive generation. MORPHOS achieves state-of-the-art performance in appearance and competitive results in geometry across multiple benchmarks, demonstrating superior generalization across various representations and robustness in long-horizon generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
