


The most promising episode for high-quality 3D generation? GaussianCube comprehensively surpasses NeRF in 3D generation

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## Figure 2. The result of digital avatar creation based on the input portrait. The method in this article can retain the identity feature information of the input portrait to a great extent, and provide detailed hairstyle and clothing modeling.
# 图 Figure 4. The result of the category condition. The 3D assets generated in this article have clear semantics and high-quality geometric structures and materials.
Thesis name: GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling Project homepage: https://gaussiancube.github.io/ Paper link: https://arxiv.org/pdf/2403.19655 Open source code: https://github.com/GaussianCube/ GaussianCube Demo video: https://www.bilibili.com/video/BV1zy411h7wB/
Specifically, assuming that the current iteration consists of


##儘管如此,透過上述擬合演算法得到的高斯仍然沒有明確的空間排列結構,這使得後續的擴散模型無法有效率地對資料進行建模。為此,研究人員提出將高斯映射到預先定義的結構化體素網格中來使得高斯具有明確的空間結構。直觀地說,這一步的目標是在盡可能保持高斯的空間相鄰關係的同時,將每個高斯 “移動” 到一個體素中。
研究人員將其建模為一個最優傳輸問題,使用Jonker-Volgenant 演算法來得到對應的映射關係,進而根據最優傳輸的解來組織將高斯組織到對應的體素中得到GaussianCube,並且用當前體素中心的偏移量取代了原始高斯的位置,以減少擴散模型的解空間。最終的 GaussianCube 表示不僅結構化,而且最大程度上保持了相鄰高斯之間的結構關係,這為 3D 生成建模的高效特徵提取提供了強有力的支持。
在三維擴散階段,本文使用三維擴散模型來建模 GaussianCube 的分佈。由於 GaussianCube 在空間上的結構化組織關係,無需複雜的網絡或訓練設計,標準的 3D 卷積足以有效提取和聚合鄰近高斯的特徵。於是,研究者利用了標準的 U-Net 網路進行擴散,並直接地將原始的 2D 操作符(包括卷積、注意力、上採樣和下採樣)替換為它們的 3D 實作。
本文的三維擴散模型也支援多種條件訊號來控制生成過程,包括類別標籤條件產生、根據圖像條件創建數位化身和根據文字產生三維數位資產。基於多模態條件的生成能力大大擴展了模型的應用範圍,並為未來的 3D 內容創造提供了強大的工具。
#研究人員首先在ShapeNet Car 資料集上驗證了GaussianCube的擬合能力。實驗結果表明,與基線方法相比,GaussianCube 可以以最快的速度和最少的參數量實現高精度的三維物體擬合。
研究人員其次在大量資料集上驗證了基於GaussianCube 的擴散模型的產生能力,包括ShapeNet、OmniObject3D、合成數位化身資料集和Objaverse 資料集。實驗結果表明,本文的模型在無條件和類別條件的物件生成、數位化身創建以及文字到 3D 合成從數值指標到視覺品質都取得了領先的結果。特別地,GaussianCube 相較之前的基線演算法實現了最高 74% 的效能提升。
1 本文的方法能夠更準確地還原輸入肖像的身份特徵、表情、配件和頭髮細節。
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