Recent advances in machine learning (ML) have raised growing privacy concerns. One notable concern of ML models is the membership leakage risk, which is defined as the extent to which an adversary can accurately determine whether a record was part of a model's training set through membership inference attacks (MIAs). While extensively studied in non-time-series models, this risk remains largely unexplored for time-series prediction models. To address this gap, this work aims to provide a precise evaluation of membership leakage risks for time-series prediction models. We first conduct an empirical analysis to identify distinct temporal discrepancies within sequential outputs between member and non-member samples in time-series models. Beyond global temporal discrepancies, our findings show that member records exhibit notable local discrepancies, including smaller fluctuations in their output sequences when actual values remain constant and more rapid adjustments when actual values change, compared with non-member records. Building on these insights, we propose TSP-MIA, a framework that leverages a patch-based attack model to capture both global and local discrepancies for more effective MIAs. Extensive evaluations demonstrate the effectiveness and robustness of TSP-MIA, revealing substantial membership leakage risks for time-series prediction models. We further assess the validity of existing defense mechanisms against TSP-MIA.

Despite the progress of 3D Gaussian Splatting (3DGS), reconstructing one-shot animatable 3D human avatars from a single image remains a challenging task. Existing 3DGS-based methods primarily rely on appearance observation and motion cues from monocular videos to reconstruct animatable 3D avatars. However, when applied to single-image setups, these methods struggle to extract accurate 3D features from a 2D image, limiting their ability to capture fine-grained appearance details and dynamic deformation, especially from challenging perspectives. In this work, we propose SIAvatar, a novel single-image human reconstruction method that integrates diffusion-based appearance prior and parametric human model geometry prior within a 3DGS framework. Secondly, a vertex-based adaptive Gaussian densification scheme is introduced to effectively represent human geometry while mitigating artifacts. Extensive experiments demonstrate that SIAvatar generates realistic 3D avatars with plausible appearance details and novel pose animation from a single input image. Project page: https://siavatar.github.io/.

3D digital garment generation and editing play a pivotal role in fashion design, virtual try-on, and gaming. Traditional methods struggle to meet the growing demand due to technical complexity and high resource costs. Learning-based approaches offer faster, more diverse garment synthesis based on specific requirements and reduce human efforts and time costs. However, they still face challenges such as inconsistent multi-view geometry or textures and heavy reliance on detailed garment topology and manual rigging. We propose SemanticGarment, a 3D Gaussian-based method that realizes high-fidelity 3D garment generation from text or image prompts and supports semantic-based interactive editing for flexible user customization. To ensure multi-view consistency and garment fitting, we propose to leverage structural human priors for the generative model by introducing a 3D semantic clothing model, which initializes the geometry structure and lays the groundwork for view-consistent garment generation and editing. Without the need to regenerate or rely on existing mesh templates, our approach allows for rapid and diverse modifications to existing Gaussians, either globally or within a local region. To address the artifacts caused by self-occlusion for garment reconstruction based on single image, we develop a self-occlusion optimization strategy to mitigate holes and artifacts that arise when directly animating self-occluded garments. Extensive experiments are conducted to demonstrate our superior performance in 3D garment generation and editing.

Collagen self-assembly supports its mechanical function, but controlling collagen mimetic peptides (CMPs) to self-assemble into higher-order oligomers with numerous functions remains challenging due to the vast potential amino acid sequence space. Herein, we developed a diffusion model to learn features from different types of human collagens and generate CMPs; obtaining 66% of synthetic CMPs could self-assemble into triple helices. Triple-helical and untwisting states were probed by melting temperature (Tm); hence, we developed a model to predict collagen Tm, achieving a state-of-art Pearson's correlation (PC) of 0.95 by cross-validation and a PC of 0.8 for predicting Tm values of synthetic CMPs. Our chemically synthesized short CMPs and recombinantly expressed long CMPs could self-assemble, with the lowest requirement for hydrogel formation at a concentration of 0.08% (w/v). Five CMPs could promote osteoblast differentiation. Our results demonstrated the potential for using computer-aided methods to design functional self-assembling CMPs.

ABSTRACT Synthesis of 3D‐aware facial images from latent spaces has garnered significant attention in multimedia content generation due to its ability to model images with rich semantics and diverse appearances. However, existing methods often rely on labeled data or suffer from incomplete attribute control and ambiguous latent space semantics. This paper proposes an efficient semantic distillation method that learns attribute directions of pre‐trained 3D GAN models, without the supervised semantic labels. We consider the latent space of GAN models as the mixture of two featured subspaces, namely the geometry‐aware space and appearance‐aware space. Following this hypothesis, we define two sets of learnable latent bases and use linear composition to represent controllable geometry and appearance feature space, respectively. To learn semantic‐wise latent bases for attribute‐controllable image generation, we design a framework and propose a three‐staged training strategy, which optimizes the appearance‐aware and the geometry‐aware latent bases. With the two sets of latent bases, we obtain the combined latent vectors using different weights for those bases and synthesize images with specified attributes. Compared to existing methods, our approach eliminates the need for labeled data and enables more controllable attribute disentanglement while ensuring identity consistency, which can be directly applied to real‐world scenarios such as virtual avatars and augmented reality applications. Experiments demonstrate the effectiveness and insight of our approach in aiding a better understanding of the latent space of 3D GANs.