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English(EN) AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation

新的AI方法增强三维渲染、重建和动画 · 已追踪4个来源

研究人员正在开发新的方法,通过整合扩散模型和先进的优化技术来增强三维渲染和重建。一种名为特征引导扩散演化(FIDE)的方法,使用Vision Transformers提取的视觉特征来指导扩散模型和CMA演化策略进行不可微逆渲染,其性能优于传统的基于梯度的方法。另一项开发是FutureSurf,它提出了一个用于动态表面重建的基准和数据集,并指出当前方法在预测观察时间窗口之外的未来几何形状方面存在困难。此外,Texture++提出了一种用于在UV空间中放大三维资产纹理的区域感知扩散模型,而AniGS则利用扩散先验来动画化由高斯溅射表示的大型三维场景,为静态重建添加细微的动态效果。 AI

影响 这些由AI驱动的三维图形领域的进步可能带来更逼真的虚拟环境、改进的资产创建流程以及更复杂AR/VR体验。

排序理由 多篇研究论文发表在arXiv上,详细介绍了用于三维渲染、重建和动画的新型AI技术。

在 Hugging Face Daily Papers 阅读 →

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新的AI方法增强三维渲染、重建和动画 · 已追踪4个来源

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多篇研究论文发表在arXiv上,详细介绍了用于三维渲染、重建和动画的新型AI技术。
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报道来源 [7]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Texture++:使用区域感知扩散模型提升3D资产纹理分辨率

    Numerous 3D assets are discarded due to low texture resolution, while current super-resolution models ignore texture maps and focus on natural images. An efficient and generalizable texture super-resolution model can revitalize a large corpus of aging yet valuable assets across i…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    未来渲染 $\neq$ 未来表面:超越观测窗口的动态表面重建基准与数据集

    Dynamic-scene reconstruction is almost always evaluated inside the observed time window, yet deployment settings such as AR overlays, robot interaction, and anticipatory planning need the future surface: the geometry at times beyond those captured. No standard benchmark measures …

  3. arXiv cs.LG TIER_1 English(EN) · Andrei-Timotei Ardelean, Michael Fischer, Tim Weyrich, Tom\'a\v{s} Iser ·

    面向非可微逆渲染的特征引导扩散

    arXiv:2607.17411v1 Announce Type: cross Abstract: Inverse rendering is traditionally solved via differentiable renderers and gradient descent, which requires substantial problem-specific engineering and is prone to getting stuck in local minima due to ambiguities. Derivative-free…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    AniGS:连接渲染与扩散先验以实现3D场景动画

    Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and lack the ambient motion that makes environments immersive. We present AniGS, a method for scene-level animation of 3D Gaussian S…

  5. arXiv cs.CV TIER_1 English(EN) · Shuaiwei Wang, Shi Li, Jieting Xu, Yuchi Huo, Qi Wang, Wenting Zheng, Rengan Xie ·

    Texture++:使用区域感知扩散模型提升3D资产纹理分辨率

    arXiv:2607.21504v1 Announce Type: new Abstract: Numerous 3D assets are discarded due to low texture resolution, while current super-resolution models ignore texture maps and focus on natural images. An efficient and generalizable texture super-resolution model can revitalize a la…

  6. arXiv cs.CV TIER_1 English(EN) · Yukun Shi, Minglun Gong ·

    未来渲染 $\neq$ 未来表面:超越观测窗口的动态表面重建基准与数据集

    arXiv:2607.21471v1 Announce Type: new Abstract: Dynamic-scene reconstruction is almost always evaluated inside the observed time window, yet deployment settings such as AR overlays, robot interaction, and anticipatory planning need the future surface: the geometry at times beyond…

  7. arXiv cs.CV TIER_1 English(EN) · Yen-Chi Cheng, Chen Gao, Chuhan Chen, Tuotuo Li, Rajvi Shah, Ayush Saraf, Changil Kim, Liangyan Gui, Alexander Schwing, Johannes Kopf, Hung-Yu Tseng ·

    AniGS:连接渲染与扩散先验以实现3D场景动画

    arXiv:2607.18539v1 Announce Type: new Abstract: Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and lack the ambient motion that makes environments immersive. We present AniGS, a m…