Synthesizing multi-view images that are geometrically consistent with a given single-view image is one of the hot issues in AIGC in recent years. Existing methods have achieved impressive performance on objects with symmetry or rigidity, but they are inappropriate for the human hand. Because an image-captured human hand has more diverse poses and less attractive textures. In this paper, we propose NP-Hand, a framework that elegantly combines the diffusion model and generative adversarial network: The multi-step diffusion is trained to synthesize low-resolution novel perspective, while the single-step generator is exploited to further enhance synthesis quality. To maintain the consistency between inputs and synthesis, we creatively introduce normal maps into NP-Hand to guide the whole synthesizing process. Comprehensive evaluations have demonstrated that the proposed framework is superior to existing state-of-the-art models and more suitable for synthesizing hand images with faithful structures and realistic appearance details. The code will be released on our website.

Grasping generation holds significant importance in both robotics and AI-generated content. While pure network paradigms based on VAEs or GANs ensure diversity in outcomes, they often fall short of achieving plausibility. Additionally, although those two-step paradigms that first predict contact and then optimize distance yield plausible results, they are always known to be time-consuming. This paper introduces a novel paradigm powered by DDPM, accommodating diverse modalities with varying interaction granularities as its generating conditions, including 3D object, contact affordance, and image content. Our key idea is that the iterative steps inherent to diffusion models can supplant the iterative optimization routines in existing optimization methods, thereby endowing the generated results from our method with both diversity and plausibility. Using the same training data, our paradigm achieves superior generation performance and competitive generation speed compared to optimization-based paradigms. Extensive experiments on both in-domain and out-of-domain objects demonstrate that our method receives significant improvement over the SOTA method. We will release the code for research purposes.

Reconstructing interacting hands from monocular images is indispensable in AR/VR applications. Most existing solutions rely on the accurate localization of each skeleton joint. However, these methods tend to be unreliable due to the severe occlusion and confusing similarity among adjacent hand parts. This also defies human perception because humans can quickly imitate an interaction pattern without localizing all joints. Our key idea is to first construct a two-hand interaction prior and recast the interaction reconstruction task as the conditional sampling from the prior. To expand more interaction states, a large-scale multimodal dataset with physical plausibility is proposed. Then a VAE is trained to further condense these interaction patterns as latent codes in a prior distribution. When looking for image cues that contribute to interaction prior sampling, we propose the interaction adjacency heatmap (IAH). Compared with a joint-wise heatmap for localization, IAH assigns denser visible features to those invisible joints. Compared with an all-in-one visible heatmap, it provides more fine-grained local interaction information in each interaction region. Finally, the correlations between the extracted features and corresponding interaction codes are linked by the ViT module. Comprehensive evaluations on benchmark datasets have verified the effectiveness of this framework. The code and dataset are publicly available at https://github.com/binghui-z/InterPrior_pytorch.

Mobile wireless sensor networks (MWSNs) have blended into a new application scenario to create revolutionary intelligent cities and handle numerous challenges of security, adaptability, and robustness as wireless technology has advanced. Exploiting lightweight cryptosystems is considered one of the main approaches for MWSNs to achieve confidentiality, integrity, and authentication to prevent malicious cyberattacks and resource abuses. The Saturnin lightweight cryptosystem, presented at ToSC in 2020, can be applied to protect MWSNs. No literature suggests Saturnin can defend against a ciphertext-only attack, where the attackers are the most susceptible. This study proposes the novel statistical differential fault analysis (SDFA) in this attack scenario with double distinguishers of square Chi-maximum likelihood estimate and Dice-Hamming weight. Following the experiments, it recovered the 256-bit secret key using 656 faults in the fourth-to-last round of Saturnin. Compared to the classic statistical fault analysis (SFA), the novel SDFA can expand fault injections to the deeper round and decrease the faults by half with a reliability of at least 99%. It gives an essential reference for figuring out how secure lightweight cryptosystems in MWSNs are.