ABSTRACT Forests are crucial terrestrial ecosystems. To understand long‐term forest community evolution driven by multiple environmental factors, we constructed a 3D forest stand spatiotemporal evolution framework featuring synchronous bidirectional coupling between terrain, hydrology, radiation, and vegetation. First, we simulated topographical evolution using a physics‐based procedural erosion method and introduced a multi‐layer soil moisture model and individual tree growth response mechanisms to reflect‐topography interactions, thereby establishing a realistic environmental basis for forest stand evolution. Second, leveraging real environmental data, we simulated growth responses and biomass changes of forest stands under different precipitation and radiation conditions, elucidated response mechanisms of individual tree attributes to environmental changes, and achieved intuitive evolution of 3D forest stands. The framework advances beyond unidirectional environmental forcing models by integrating hydraulic erosion, soil moisture dynamics, and slope‐aware radiation within a unified monthly timestep, enabling co‐evolutionary simulation of forest stands under dynamic landscapes. Finally, the computer‐based model incorporated natural disaster events such as fires and droughts, with real‐time interaction and visualization capabilities, supporting immersive and responsive forest landscape simulation and detailed spatiotemporal evolution analysis, enabling assessment of the dynamic recovery processes of forest stands under multiple disturbance scenarios.

Garment is an essential component of digital humans, and the accurate representation of dynamic simulation details and wrinkle characteristics is crucial for enhancing the realism of virtual scenes. However, this task remains significantly challenging in complex simulation scenarios. Therefore, we propose a novel garment simulation method based on Graph Neural Networks (GNNs), referred to as SAGS-GNN, which effectively simulates self-collision and cloth anisotropy. To tackle the self-collision problem, we present the repulsive loss term and the maximum depth loss term. These terms effectively simulate the interactions between the vertices of the cloth mesh by jointly constraining their positions, thereby facilitating the self-collision handling of garments. Furthermore, our approach utilizes the Neo-Hookean StVK method to achieve anisotropy in cloth, further reflecting the different wrinkle details of multiple materials during motion. In summary, our SAGS method effectively mitigates the issue of interpenetration among garments, facilitates the realization of anisotropic properties in a variety of fabric materials, and significantly enhances the visual realism of virtual apparel. We evaluate our method on various garment types and materials, demonstrating competitive qualitative and quantitative results.

Abstract We specifically present a general method for monocular fluid videos to reconstruct and edit 3D fluid volume. Although researchers have developed many monocular video‐based methods, the reconstructed results are merely one layer of geometry surface, lack of accurate physical attributes of fluids, and challenging to edit fluid. We obtain a high‐quality 3D fluid volume by extending the smoothed particle hydrodynamics (SPH) model with external force guidance. For reconstructing fluid, we design target particles that are recovered from the shape from shading (SFS) method and initialize fluid particles that are spatially consistent with target particles. For editing fluid, we translate the deformation of target particles into the 3D fluid volume by merging user‐specified features of interest. Separating the low‐ and high‐frequency height field allows us to efficiently solve the motion equations for a liquid while retaining enough details to obtain realistic‐looking behaviours. Our experimental results compare favourably to the state‐of‐the‐art in terms of global fluid volume motion features and fluid surface details and demonstrate our model can achieve desirable and pleasing effects.