Constructing drivable and photorealistic 3D head avatars has become a central task in$A R / X R$, enabling immersive and expressive user experiences. With the emergence of high-fidelity, efficient representations such as$3 D$Gaussians, recent works have pushed toward ultra-detailed head avatars. Existing approaches typically fall into two categories: rule-based analytic rigging or neural networkbased deformation fields. While effective in constrained settings, both approaches often fail to generalize to unseen expressions and poses-particularly in extreme reenactment scenarios. Other methods constrain Gaussians to the global texel space of 3DMMs to reduce rendering complexity. However, these texel-based avatars tend to underutilize the underlying mesh structure. They apply minimal analytic deformation and rely heavily on neural regressors and heuristic regularization in UV space, which weakens geometric consistency and limits extrapolation to complex, out-of-distribution deformations. To address these limitations, we introduce TexAvatars, a hybrid avatar representation that combines the explicit geometric grounding of analytic rigging with the spatial continuity of texel space. Our approach predicts local geometric attributes in UV space via CNNs, but drives 3D deformation through mesh-aware Jacobians, enabling smooth and semantically meaningful transitions across triangle boundaries. This hybrid design separates semantic modeling from geometric control, resulting in improved generalization, interpretability, and stability. Furthermore, TexAvatars captures fine-grained expression effects-including muscle-induced wrinkles, glabellar lines, and realistic mouth cavity geom-etry-with high fidelity. Our method achieves state-of-theart performance under extreme pose and expression vari-ations, demonstrating strong generalization in challenging head reenactment settings.

The increasing demand for AR/VR applications has highlighted the need for high-quality content, such as 360° live wallpapers. However, generating high-quality 360° panoramic contents remains a challenging task due to the severe distortions introduced by equirectangular projection (ERP). Existing approaches either fine-tune pretrained diffusion models on limited ERP datasets or adopt tuning-free methods that still rely on ERP latent representations, often resulting in distracting distortions near the poles. In this paper, we introduce SphereDiff, a novel approach for synthesizing 360° static and live wallpaper with state-of-the-art diffusion models without additional tuning. We define a spherical latent representation that ensures consistent quality across all perspectives, including near the poles. Then, we extend MultiDiffusion to spherical latent representation and propose a dynamic spherical latent sampling method to enable direct use of pretrained diffusion models. Moreover, we introduce distortion-aware weighted averaging to further improve the generation quality. Our method outperforms existing approaches in generating 360° static and live wallpaper, making it a robust solution for immersive AR/VR applications.

With growing demand in media and social networks for personalized images, the need for advanced head-swapping techniques—integrating an entire head from the head image with the body from the body image—has increased. However, traditional head-swapping methods heavily rely on face-centered cropped data with primarily frontal-facing views, which limits their effectiveness in real-world applications. Additionally, their masking methods, designed to indicate regions requiring editing, are optimized for these types of dataset but struggle to achieve seamless blending in complex situations, such as when the original data includes features like long hair extending beyond the masked area. To overcome these limitations and enhance adaptability in diverse and complex scenarios, we propose a novel head swapping method, HID, that is robust to images including the full head and the upper body, and handles from frontal to side views, while automatically generating context-aware masks. For automatic mask generation, we introduce the IOMask, which enables seamless blending of the head and body, effectively addressing integration challenges. We further introduce the hair injection module to capture hair details with greater precision. Our experiments demonstrate that the proposed approach achieves state-of-the-art performance in head swapping, providing visually consistent and realistic results across a wide range of challenging conditions.

Recent advancements in head avatar rendering using Gaussian primitives have achieved significantly high-fidelity results. Although precise head geometry is crucial for applications like mesh reconstruction and relighting, current methods struggle to capture intricate geometric details and render unseen poses due to their reliance on similarity transformations, which cannot handle stretch and shear transforms essential for detailed deformations of geometry. To address this, we propose SurFhead, a novel method that reconstructs riggable head geometry from RGB videos using 2D Gaussian surfels, which offer well-defined geometric properties, such as precise depth from fixed ray intersections and normals derived from their surface orientation, making them advantageous over 3D counterparts. SurFhead ensures high-fidelity rendering of both normals and images, even in extreme poses, by leveraging classical mesh-based deformation transfer and affine transformation interpolation. SurFhead introduces precise geometric deformation and blends surfels through polar decomposition of transformations, including those affecting normals. Our key contribution lies in bridging classical graphics techniques, such as mesh-based deformation, with modern Gaussian primitives, achieving state-of-the-art geometry reconstruction and rendering quality. Unlike previous avatar rendering approaches, SurFhead enables efficient reconstruction driven by Gaussian primitives while preserving high-fidelity geometry.

This study focuses on the robotic capability of ungrasping, or releasing, an object in a grasp from the gripper to the robot's environment. The presented technique enables the delicate release of a grasped object using non-static contacts, allowing for rolling and/or sliding. This dexterous manipulation capability is particularly relevant when ungrasping thin or slender objects, as will be demonstrated with real examples. We initially discuss the establishment of three-dimensional stability during ungrasping manipulation, ensuring robustness. Subsequently, we present a planning and control solution for three-dimensional ungrasping, building upon our previous planar version. A series of experiments across various test scenarios, ranging from precision placement to puzzle tiling, showcase the viability and effectiveness of our approach.

Fifth-generation (5G) networks are widely applied in diverse fields, and numerous devices are densely interconnected in these networks. As the number of devices increases, security and authentication concerns become more important for this technology. Furthermore, the concurrent connections of many devices result in severe signaling congestion, and the 5G-AKA protocol has certain vulnerabilities. In 2020, Cao et al. proposed a novel scheme referred to as LSAA that employs the Chebyshev chaotic map, which significantly alleviated the aforementioned problems and fulfilled security requirements. However, their scheme overlooked critical security aspects like untraceability. In this study, we proposed Chebyshev-polynomials-based efficient and secure access authentication (CESA), an efficient and secure access authentication scheme inspired by the lightweight and secure access authentication scheme for both user equipment (UE) and massive machine-type communication devices in 5G networks. We achieve improved security properties, efficiency, and lightweight characteristics compared to existing protocols. CESA includes a lightweight authentication method using an extended Chebyshev chaotic map, ensuring resistance to various attacks for common UE and massive machine-type communication devices. CESA offers robust security features, including mutual authentication, identity anonymity, secure session key agreement, and a perfect forward secrecy/public key-based scheme, all achieved with high efficiency and low computational cost. We conducted a rigorous evaluation of CESA using the formal security analysis tools ProVerif and Scyther. This system reduces bandwidth consumption, signaling and transmission cost, computational cost, and storage cost by 75.98% on average.

Despite the remarkable advancements in head reenactment, the existing methods face challenges in cross-domain head reenactment, which aims to transfer human motions to domains outside the human, including cartoon characters. It is still difficult to extract motion from out-of-domain images due to the distinct appearances, such as large eyes. Recently, previous work introduced a large-scale anime dataset called AnimeCeleb and a cross-domain head reenactment model, including an optimization-based mapping function to translate the human domain’s expressions to the anime domain. However, we found that the mapping function, which relies on a subset of expressions, imposes limitations on the mapping of various expressions. To solve this challenge, we introduce a novel expression domain translation network that transforms human expressions into anime expressions. Specifically, to maintain the geometric consistency of expressions between the input and output of the expression domain translation network, we employ a 3D geometric-aware loss function that reduces the distances between the vertices in the 3D mesh of the human and anime. By doing so, it forces high-fidelity and one-to-one mapping with respect to two cross-expression domains. Our method outperforms existing methods in both qualitative and quantitative analysis, marking a significant advancement in the field of cross-domain head reenactment.

Although robotic portrait drawing has been a recurring topic in robotics, most robotic portrait drawing systems have focused on either speed or quality of the drawing due to various technical difficulties in pursuing both goals. In this work, we propose a novel robotic portrait drawing system that uses advanced machine-learning techniques and a variable line width Chinese calligraphy pen to draw a high-quality portrait in a short time. Our approach first detects the human keypoints from the incoming video stream and extracts the dominant human face from the video, and then uses a CycleGAN based algorithm to convert the image style into a black-and-white line drawing. After a number of optimization steps, we use a 6-DOF robotic arm and a calligraphy pen to quickly draw the portrait. The system has been openly demonstrated to the general public at the RoboWorld 2022 exhibition, where the system has drawn portraits of more than 40 visitors with a satisfaction rate of 95%.