Despite recent advancements in native latent diffusion models for single-view 3D generation, they still suffer from inadequate geometric details due to the global entanglement of VecSet-based representations and inaccurate structural topology arising from single-view ambiguity. Existing solutions often rely on model scaling over large-scale datasets to improve fidelity, which is computationally prohibitive and inefficient in capturing fine-grained details. In this work, we propose Tempo3D , a resource-efficient paradigm for high-fidelity VecSet-based 3D generation via efficient temporal-aware fine-tuning and multi-view latent aggregation. Our approach consists of two critical components: (1) To facilitate robust detail learning, we establish the TempoDetail dataset and design the Time-Segmented LoRA (TS-LoRA) module. TS-LoRA exploits the inherent "structure-to-details" temporal transition of flow-based models to decouple feature learning, significantly bolstering the capacity to produce high-frequency details without large-scale retraining. (2) To mitigate geometric inaccuracies stemming from single-view ambiguity, we propose a multi-view latent aggregation strategy incorporating Soft Flow Trajectory Projection (Soft FTP) and Score-based Velocity Field Aggregation (SVFA). By aggregating reference views derived from Multimodal Large Language Models (MLLMs), these components resolve the intrinsic conflicts between velocity vectors caused by cross-view latent discrepancies, thereby preventing geometric collapse and ensuring directional consistency. Moreover, the integration of MLLMs empowers Tempo3D to support precise semantic 3D editing via natural language prompts while maintaining structural integrity. Experiments demonstrate that Tempo3D outperforms state-of-the-art baselines in generation quality, geometric precision, and editability under a highly efficient computational budget.

Existing 3D Gaussian (3DGS) based methods tend to produce blurriness and artifacts on delicate textures (small objects and high-frequency textures) in aerial large-scale scenes. The reason is that the delicate textures usually occupy a relatively small number of pixels, and the accumulated gradients from loss function are difficult to promote the splitting of 3DGS. To minimize the rendering error, the model will use a small number of large Gaussians to cover these details, resulting in blurriness and artifacts. To solve the above problem, we propose a novel hierarchical Gaussian: JumpingGS. JumpingGS assigns different levels to Gaussians to establish a hierarchical representation. Low-level Gaussians are responsible for the coarse appearance, while high-level Gaussians are responsible for the details. First, we design a splitting strategy that allows low-level Gaussians to skip intermediate levels and directly split the appropriate high-level Gaussians for delicate textures. This level-jump splitting ensures that the weak gradients of delicate textures can always activate a higher level instead of being ignored by the intermediate levels. Second, JumpingGS reduces the gradient and opacity thresholds for density control according to the representation levels, which improves the sensitivity of high-level Gaussians to delicate textures. Third, we design a novel training strategy to detect training views in hard-to-observe regions, and train the model multiple times on these views to alleviate underfitting. Experiments on aerial large-scale scenes demonstrate that JumpingGS outperforms existing 3DGS-based methods, accurately and efficiently recovering delicate textures in large scenes.