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English(EN) MORPHOS: Autoregressive 4D Generation with Temporal Structured Latents

新论文探讨视觉和 4D 生成模型的效率问题

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文探讨了视觉和 4D 资产自回归生成方面的进展。第一篇论文《Where to Refine, When to Stop》介绍了一个名为 LD-Pruning 的免训练框架，通过识别和移除冗余计算来显著降低视觉自回归模型的推理延迟。第二篇论文《MORPHOS》提出了一个新颖的自回归框架，用于从视频生成动态 3D 资产，支持多种表示并提高时间一致性。 AI

影响 这些论文引入了新颖的技术，以提高自回归生成模型在视觉和 4D 内容创建方面的效率和能力。

排序理由 两篇在 arXiv 上发表的学术论文，详细介绍了生成模型的新方法。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新论文探讨视觉和 4D 生成模型的效率问题

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两篇在 arXiv 上发表的学术论文，详细介绍了生成模型的新方法。
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报道来源 [3]

  1. arXiv cs.CV TIER_1 English(EN) · Changwang Mei, Peisong Wang, Zekun Li, Changsheng Li, Shuang Qiu, Qinghao Hu, Gang Li, Yifan Zhang, Zhihui Wei, Jian Cheng ·

    何处优化，何时停止：通过潜在差异重思冗余以实现高效视觉自回归生成

    arXiv:2606.00310v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models deliver high-quality image generation but suffer from significant inference latency at high resolutions. Recent acceleration approaches most rely on heuristic measures with layer features to prune …

  2. arXiv cs.CV TIER_1 English(EN) · Minkyung Kwon, Jinhyeok Choi, Youngjin Shin, Jaeyeong Kim, JongMin Lee, Seungryong Kim ·

    MORPHOS：具有时间结构化潜在变量的自回归四维生成

    arXiv:2606.02491v1 Announce Type: new Abstract: 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 singl…

  3. arXiv cs.CV TIER_1 English(EN) · Seungryong Kim ·

    MORPHOS：具有时间结构化潜变量的自回归 4D 生成

    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 …

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