arXiv subjects

Shuangjiu Xiao

Publications and source records attributed to Shuangjiu Xiao.

4 recordsLinked to original sources

arXiv2026

SECA: Strict-Elastic Compact Activation for Differentiable Elastoplastic Deformation

Return mapping keeps an exact elastic domain but switches its response tangent at yield, while smoothing laws with a sub-yield tail admit plastic flow below the threshold. We present SECA, a Strict-Elastic Compact Activation method for differentiable elastoplastic simulation. SECA prescribes a bounded $C^2$ activation over a finite positive-overstress interval and integrates it into a Perzyna-type flow law, so the implicit update returns exactly zero plastic increment for sub-yield trials while smoothing the onset. For scalar and proportional isotropic $J_2$ updates we derive an exact decomposition of the plastic-increment discrepancy into finite-width and viscosity contributions, and an explicit algebraic budget for the transition width. The update is embedded in finite-deformation loading histories with contact and complete unloading, and differentiated to released equilibria. Controlled indentation results stay closely matched. In the tested coarse cube-indentation run, where the transition is crossed without intermediate increments, SECA reduces whole-run Newton iterations by 47% and backtracking halvings by 93% against return mapping. A four-method comparison evaluates released-shape error and computational cost across loading partitions, and on a contact scene the path derivative drives a bounded control task to its target in three iterations. Further examples show elastic recovery and residual deformation under compression, tension and bending.

cs.GR↗

arXiv2025

HiGS: Hierarchical Generative Scene Framework for Multi-Step Associative Semantic Spatial Composition

Three-dimensional scene generation holds significant potential in gaming, film, and virtual reality. However, most existing methods adopt a single-step generation process, making it difficult to balance scene complexity with minimal user input. Inspired by the human cognitive process in scene modeling, which progresses from global to local, focuses on key elements, and completes the scene through semantic association, we propose HiGS, a hierarchical generative framework for multi-step associative semantic spatial composition. HiGS enables users to iteratively expand scenes by selecting key semantic objects, offering fine-grained control over regions of interest while the model completes peripheral areas automatically. To support structured and coherent generation, we introduce the Progressive Hierarchical Spatial-Semantic Graph (PHiSSG), which dynamically organizes spatial relationships and semantic dependencies across the evolving scene structure. PHiSSG ensures spatial and geometric consistency throughout the generation process by maintaining a one-to-one mapping between graph nodes and generated objects and supporting recursive layout optimization. Experiments demonstrate that HiGS outperforms single-stage methods in layout plausibility, style consistency, and user preference, offering a controllable and extensible paradigm for efficient 3D scene construction.

cs.CV↗

arXiv2025

Real-Time Interactive Hybrid Ocean: Spectrum-Consistent Wave Particle-FFT Coupling

Fast Fourier Transform-based (FFT) spectral oceans are widely adopted for their efficiency and large-scale realism, but they assume global stationarity and spatial homogeneity, making it difficult to represent non-uniform seas and near-field interactions (e.g., ships and floaters). In contrast, wave particles capture local wakes and ripples, yet are costly to maintain at scale and hard to match global spectral statistics.We present a real-time interactive hybrid ocean: a global FFT background coupled with local wave-particle (WP) patch regions around interactive objects, jointly driven under a unified set of spectral parameters and dispersion. At patch boundaries, particles are injected according to the same directional spectrum as the FFT, aligning the local frequency-direction distribution with the background and matching energy density, without disturbing the far field.Our approach introduces two main innovations: (1) Hybrid ocean representation. We couple a global FFT background with local WP patches under a unified spectrum, achieving large-scale spectral consistency while supporting localized wakes and ripples.(2) Frequency-bucketed implementation. We design a particle sampling and GPU-parallel synthesis scheme based on frequency buckets, which preserves spectral energy consistency and sustains real-time interactive performance.Together, these innovations enable a unified framework that delivers both large-scale spectral realism and fine-grained interactivity in real time.

eess.SP↗

arXiv2018

Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.

cs.LG↗