Cyc3D：基于循环一致性正则化的细粒度可控3D生成
Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization
AI总结：
Cyc3D框架通过引入循环一致性正则化，利用视图一致性和条件一致性双重约束，显著提升了细粒度可控3D生成的对齐精度与可控性。
摘要：
尽管3D生成取得了显著进展，但实现可控性，即确保生成的3D内容与边缘和深度等输入条件之间的一致性，仍然是一个重大挑战。现有方法通常难以保持准确的对齐，导致明显的差异。为了解决这个问题，我们提出了Cyc3D，这是一个新框架，通过显式地鼓励基于从一阶生成中提取的信号生成的二阶3D内容与其原始输入控制之间的循环一致性，来增强可控3D生成。具体而言，我们采用一个高效的feed-forward主干网络，能够根据输入条件和文本提示生成3D对象。给定初始视角和控制信号，从生成的3D内容中渲染出一个新视角，从中提取条件用于重新生成3D内容。这个重新生成的输出随后被渲染回初始视角，接着进行另一轮控制信号提取，形成一个具有两个一致性约束的循环过程。视图一致性通过语义相似度测量来确保两个生成的3D对象之间的连贯性，以适应生成多样性。条件一致性将最终提取的信号与原始输入控制对齐，在整个过程中保持结构或几何细节。在流行基准上的大量实验表明，Cyc3D显著提高了可控性，特别是对于细粒度细节，在各种条件下均优于现有方法（例如，边缘的PSNR提高14.17%，草图的PSNR提高6.26%）。
英文摘要：
Despite the remarkable progress of 3D generation, achieving controllability, i.e., ensuring consistency between generated 3D content and input conditions like edge and depth, remains a significant challenge. Existing methods often struggle to maintain accurate alignment, leading to noticeable discrepancies. To address this issue, we propose \name{}, a new framework that enhances controllable 3D generation by explicitly encouraging cyclic consistency between the second-order 3D content, generated based on extracted signals from the first-order generation, and its original input controls. Specifically, we employ an efficient feed-forward backbone that can generate a 3D object from an input condition and a text prompt. Given an initial viewpoint and a control signal, a novel view is rendered from the generated 3D content, from which the extracted condition is used to regenerate the 3D content. This re-generated output is then rendered back to the initial viewpoint, followed by another round of control signal extraction, forming a cyclic process with two consistency constraints. \emph{View consistency} ensures coherence between the two generated 3D objects, measured by semantic similarity to accommodate generative diversity. \emph{Condition consistency} aligns the final extracted signal with the original input control, preserving structural or geometric details throughout the process. Extensive experiments on popular benchmarks demonstrate that \name{} significantly improves controllability, especially for fine-grained details, outperforming existing methods across various conditions (e.g., +14.17\% PSNR for edge, +6.26\% PSNR for sketch).
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。
