arXiv subjects

Xiaolin Zhang

Publications and source records attributed to Xiaolin Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Split and Drive: Dual-Axis Disentanglement for Real-Time Gaussian Head Avatars

Creating photorealistic animatable head avatars from a single image remains a fundamental challenge in digital human synthesis. While recent 3D Gaussian Splatting methods have achieved promising results, they rely on external tracking pipelines whose latency is excluded from inference measurements. Furthermore, they adopt unified representations that entangle geometrically distinct facial regions, limiting both expressiveness and rendering fidelity. We propose SpiD (Split and Drive), a single-image Gaussian head avatar framework built on two disentanglement axes. The compute axis internalizes per-frame driving, eliminating external tracking dependency at inference. The feature axis decomposes the avatar into three specialized Gaussian branches, each modeling a geometrically distinct facial domain. Extensive experiments demonstrate consistently strong performance against state-of-the-art methods while achieving the fastest inference speed among all compared methods on a single GPU with the complete driving pipeline included.

cs.CV↗

arXiv2026

TongueReenact: Geometry-Anchored Tongue Synthesis for Face Reenactment

Modern face reenactment systems achieve impressive pose and expression transfer using geometry-driven representations. However, they largely ignore tongue dynamics, leading to anatomically inconsistent mouth interiors during speech and expressive motions. We introduce the first framework for cross-identity tongue dynamics transfer in face reenactment. We propose a foundation-model-assisted bootstrapping pipeline that produces a dedicated tongue segmentation model for in-the-wild reenactment without curated annotations. We further introduce a spatially constrained latent masked diffusion model for realistic tongue synthesis, with adaptive mask dilation for seamless mouth boundary transitions. Extensive experiments demonstrate improvements of more than two times over all baselines on every tongue-specific metric. We additionally propose a VLM-based evaluation protocol that replicates expert annotation at scale, confirming perceptual superiority across all ablation variants.

cs.CV↗

arXiv2026

Bifrost: Hybrid TEE-FHE Inference for Privacy-Preserving Transformer and LLM Serving

Cloud-hosted transformer and large language model (LLM) inference creates a direct confidentiality problem: user prompts may contain sensitive code, business data, personal information, or regulated documents, yet remote serving exposes intermediate state to the cloud software stack and accelerator runtime. Fully homomorphic encryption (FHE) keeps accelerator-side execution ciphertext-only, but end-to-end LLM inference remains expensive because linear layers are interleaved with non-linear, cache-state, and refresh-sensitive operators. CPU trusted execution environments (TEEs) can execute those operators natively, but a CPU TEE alone does not define how an untrusted accelerator should participate. We present Bifrost, a hybrid TEE-FHE serving architecture in which secrets are provisioned only to an attested CPU TEE, while the accelerator, device memory, driver/runtime stack, and host software remain outside the trusted computing base. Bifrost uses FHE as a secure delegation mechanism for projection and feed-forward linear layers on accelerator-backed CKKS, while non-linear operators, attention-side control logic, KV-state transitions, and decrypt-then-encrypt refresh execute inside the CPU TEE. Bifrost+ further applies a prefill/decode split: prompt-side KV state is built inside the CPU TEE, and only decode-side state enters the hybrid ciphertext path. In an estimator-style comparison matching Euston's methodology, Bifrost reduces projected latency by 9.25x on GPT-2 (1.5B) and 9.91x on LLaMA 3 (8B). In direct CKKS/FHE deployments, Bifrost+ reduces TTFT by 14.6-45.8x on GPT-2 (124M) and 15.3-53.4x on Qwen3 (0.6B). The systems lesson is selective encrypted execution: use FHE only where ciphertext-only accelerator delegation is required, and keep non-linear, refresh, and prompt-side work inside the CPU TEE.

cs.CR↗

arXiv2026

The new generation lunar gravitational wave detectors: sky map resolution and joint analysis

Lunar-based gravitational-wave interferometry is a fascinating endeavor, and was proposed as a promising approach to bridge the observational gap between space-borne and ground-based detectors. In this work, we adopt the Fisher-matrix method to examine the angular-resolution performance of the newly proposed Crater Interferometry Gravitational-wave Observatory (CIGO) on the lunar crater rim near the north pole, together with TianQin and LISA, for monochromatic sources in the 0.1-10 Hz band. We find that above 0.1 Hz, CIGO achieves better localization accuracy than the other two space-based missions and dominates the combined detector network's performance, provided that lunar noise mitigation is achieved in the 0.1-2.87 Hz frequency range. We further explore an upgraded Tetrahedron configuration, TCIGO, with a fourth station at the bottom of a crater, which forms a regular tetrahedral constellation on the lunar surface. The result shows that TCIGO yields a five-fold improvement in angular-resolution capability over CIGO and gets better sky coverage across the target frequency band.

gr-qc↗

arXiv2026

Superradiance in acoustic black hole

Rotating superradiance in cylindrical geometries has recently been observed experimentally using acoustic waves, shedding light on the superradiant phenomenon in black holes. In this paper, we study superradiance in acoustic black holes made with solid material for the first time, using theoretical analysis and numerical simulations in COMSOL Multiphysics. We find that superradiance can occur in acoustic black holes when the general superradiance condition is met. We also find that the amplification effect is significantly weaker in acoustic black holes than in regular cylinders, due to absorption within the black holes. Furthermore, we have found that different acoustic black hole models exhibit similar superradiance behavior at the same physical scale, which is also consistent with the phenomena in extremal Kerr black holes. We also present that the solid material ABH model has the most degrees of freedom.

gr-qc↗

arXiv2025

Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion

Unsupervised monocular depth estimation has received widespread attention because of its capability to train without ground truth. In real-world scenarios, the images may be blurry or noisy due to the influence of weather conditions and inherent limitations of the camera. Therefore, it is particularly important to develop a robust depth estimation model. Benefiting from the training strategies of generative networks, generative-based methods often exhibit enhanced robustness. In light of this, we employ a well-converging diffusion model among generative networks for unsupervised monocular depth estimation. Additionally, we propose a hierarchical feature-guided denoising module. This model significantly enriches the model's capacity for learning and interpreting depth distribution by fully leveraging image features to guide the denoising process. Furthermore, we explore the implicit depth within reprojection and design an implicit depth consistency loss. This loss function serves to enhance the performance of the model and ensure the scale consistency of depth within a video sequence. We conduct experiments on the KITTI, Make3D, and our self-collected SIMIT datasets. The results indicate that our approach stands out among generative-based models, while also showcasing remarkable robustness.

cs.CV↗

arXiv2025

AvatarBack: Back-Head Generation for Complete 3D Avatars from Front-View Images

Recent advances in Gaussian Splatting have significantly boosted the reconstruction of head avatars, enabling high-quality facial modeling by representing an 3D avatar as a collection of 3D Gaussians. However, existing methods predominantly rely on frontal-view images, leaving the back-head poorly constructed. This leads to geometric inconsistencies, structural blurring, and reduced realism in the rear regions, ultimately limiting the fidelity of reconstructed avatars. To address this challenge, we propose AvatarBack, a novel plug-and-play framework specifically designed to reconstruct complete and consistent 3D Gaussian avatars by explicitly modeling the missing back-head regions. AvatarBack integrates two core technical innovations,i.e., the Subject-specific Generator (SSG) and the Adaptive Spatial Alignment Strategy (ASA). The former leverages a generative prior to synthesize identity-consistent, plausible back-view pseudo-images from sparse frontal inputs, providing robust multi-view supervision. To achieve precise geometric alignment between these synthetic views and the 3D Gaussian representation, the later employs learnable transformation matrices optimized during training, effectively resolving inherent pose and coordinate discrepancies. Extensive experiments on NeRSemble and K-hairstyle datasets, evaluated using geometric, photometric, and GPT-4o-based perceptual metrics, demonstrate that AvatarBack significantly enhances back-head reconstruction quality while preserving frontal fidelity. Moreover, the reconstructed avatars maintain consistent visual realism under diverse motions and remain fully animatable.

cs.CV↗

arXiv2025

Plant-Centric Metaverse: A Biocentric-Creation Framework for Ecological Art and Digital Symbiosis

Digital ecological art represents an emergent frontier where biological media converge with virtual environments. This study examines the paradigm shift from anthropocentric to plant-centered artistic narratives within the metaverse, contextualizing how digital platforms transform ecological expression. However, current frameworks fail to systematically guide artists in leveraging plant agency for digital symbiosis that transcends human-centered creation. We propose the Biocentric-Creation Transformation Ideology (BCTI) framework and validate it through multimodal case studies spanning bio-art, NFTs, and VR ecosystems (2013-2023). Our analysis reveals: (1) Metaverse ecosystems enable unprecedented plant-algorithm co-creation, with biological artworks increasing by 133% in premier archives (2020 vs 2013); (2) Digital symbiosis manifests through blockchain DAOs where plants govern human-plant collaborations; (3) Algorithmic photosynthesis in VR environments reshapes ecological aesthetics through real-time biodata translation. The BCTI framework advances ecological art theory by systematizing the transition from representation to plant-centered agency, offering artists a blueprint for post-anthropocene creation. This redefines environmental consciousness in virtual realms while establishing new protocols for cross-species digital collaboration.

cs.HC↗

arXiv2025

AvatarMakeup: Realistic Makeup Transfer for 3D Animatable Head Avatars

Similar to facial beautification in real life, 3D virtual avatars require personalized customization to enhance their visual appeal, yet this area remains insufficiently explored. Although current 3D Gaussian editing methods can be adapted for facial makeup purposes, these methods fail to meet the fundamental requirements for achieving realistic makeup effects: 1) ensuring a consistent appearance during drivable expressions, 2) preserving the identity throughout the makeup process, and 3) enabling precise control over fine details. To address these, we propose a specialized 3D makeup method named AvatarMakeup, leveraging a pretrained diffusion model to transfer makeup patterns from a single reference photo of any individual. We adopt a coarse-to-fine idea to first maintain the consistent appearance and identity, and then to refine the details. In particular, the diffusion model is employed to generate makeup images as supervision. Due to the uncertainties in diffusion process, the generated images are inconsistent across different viewpoints and expressions. Therefore, we propose a Coherent Duplication method to coarsely apply makeup to the target while ensuring consistency across dynamic and multiview effects. Coherent Duplication optimizes a global UV map by recoding the averaged facial attributes among the generated makeup images. By querying the global UV map, it easily synthesizes coherent makeup guidance from arbitrary views and expressions to optimize the target avatar. Given the coarse makeup avatar, we further enhance the makeup by incorporating a Refinement Module into the diffusion model to achieve high makeup quality. Experiments demonstrate that AvatarMakeup achieves state-of-the-art makeup transfer quality and consistency throughout animation.

cs.CV↗

arXiv2025

InstaFace: Identity-Preserving Facial Editing with Single Image Inference

Facial appearance editing is crucial for digital avatars, AR/VR, and personalized content creation, driving realistic user experiences. However, preserving identity with generative models is challenging, especially in scenarios with limited data availability. Traditional methods often require multiple images and still struggle with unnatural face shifts, inconsistent hair alignment, or excessive smoothing effects. To overcome these challenges, we introduce a novel diffusion-based framework, InstaFace, to generate realistic images while preserving identity using only a single image. Central to InstaFace, we introduce an efficient guidance network that harnesses 3D perspectives by integrating multiple 3DMM-based conditionals without introducing additional trainable parameters. Moreover, to ensure maximum identity retention as well as preservation of background, hair, and other contextual features like accessories, we introduce a novel module that utilizes feature embeddings from a facial recognition model and a pre-trained vision-language model. Quantitative evaluations demonstrate that our method outperforms several state-of-the-art approaches in terms of identity preservation, photorealism, and effective control of pose, expression, and lighting.

cs.CV↗

arXiv2025

Now Let's Make It Physical: Enabling Physically Trusted Certificate Issuance for Keyless Security in CAs

The signing key protection of Certificate Authorities (CAs) remains a critical challenge in PKI. Traditional approaches struggle to eliminate the risk of key exposure due to those (un)intentional human errors. This long-standing dilemma motivates us to propose Armored Core, a novel PKI security extension using the trusted binding of Physically Unclonable Function (PUF) for CAs. PUFs leverage manufacturing variations to generate unique and random responses. Combining with XOR and hash, they can make key exposure impossible for CAs through keyless certificate issuance. In Armored Core, we design a set of PUF-based X.509v3 certificate functions for CAs to generate physically trusted "signatures" without using a digital key. Moreover, we introduce a novel PUF transparency mechanism to effectively monitor the PUF operations in CAs. We integrate Armored Core into real-world PKI systems including Let's Encrypt Pebble and Certbot. We also provide a PUF-embedded hardware prototype. The evaluation results show that Armored Core can achieve keyless certificate issuance while improving the computation performance by 4.9%~73.7%. It only incurs small communication and storage overhead (<4%).

cs.CR↗

arXiv2025

Environmental Factors Can Have Opposite Biodiversity Influences on the Community Temporal Stability In Aquatic Ecosystems

1. An understanding of how biodiversity confers ecosystem stability is crucial in managing ecosystems under major environmental changes. Multiple biodiversity drivers can stabilize ecosystem functions over time. However, we know little about how local environmental conditions can influence these biodiversity drivers, and consequently how they indirectly shape the ecological stability of ecosystems. 2. We hypothesized that environmental factors can have opposite influences (i.e., not necessarily either positive or negative) on the temporal stability of communities in different environmental ranges depending on the biodiversity drivers involved. We tested this novel hypothesis by using data from a 4-year-long field study of submerged macrophyte across a water depth gradient in 8 heterogeneous bays of Erhai lake (with total sample size of 30,071 quadrats), a large lentic system in China. 3. Results indicate that a unimodal pattern of stability in temporal biomass measurements occurred along the water-depth gradient, and that multiple biodiversity drivers (the asynchrony in species dynamics, and the stability of dominant species) generally increased the temporal stability of aquatic primary producers. However, the effect of water depth either increased or decreased the stability of biomass according to the environmental conditions associated with sites along the water depth gradient. 4. Synthesis. These results reveal the influence of local environmental conditions on the biodiversity drivers of stability may help predict the functional consequences of biodiversity change across different scenarios of environmental change.

q-bio.PE↗

arXiv2025

DreamLCM: Towards High-Quality Text-to-3D Generation via Latent Consistency Model

Recently, the text-to-3D task has developed rapidly due to the appearance of the SDS method. However, the SDS method always generates 3D objects with poor quality due to the over-smooth issue. This issue is attributed to two factors: 1) the DDPM single-step inference produces poor guidance gradients; 2) the randomness from the input noises and timesteps averages the details of the 3D contents. In this paper, to address the issue, we propose DreamLCM which incorporates the Latent Consistency Model (LCM). DreamLCM leverages the powerful image generation capabilities inherent in LCM, enabling generating consistent and high-quality guidance, i.e., predicted noises or images. Powered by the improved guidance, the proposed method can provide accurate and detailed gradients to optimize the target 3D models. In addition, we propose two strategies to enhance the generation quality further. Firstly, we propose a guidance calibration strategy, utilizing Euler Solver to calibrate the guidance distribution to accelerate 3D models to converge. Secondly, we propose a dual timestep strategy, increasing the consistency of guidance and optimizing 3D models from geometry to appearance in DreamLCM. Experiments show that DreamLCM achieves state-of-the-art results in both generation quality and training efficiency. The code is available at https://github.com/1YimingZhong/DreamLCM.

cs.CV↗

arXiv2024

RISecure-PUF: Multipurpose PUF-Driven Security Extensions with Lookaside Buffer in RISC-V

RISC-V's limited security features hinder its use in confidential computing and heterogeneous platforms. This paper introduces RISecure-PUF, a security extension utilizing existing Physical Unclonable Functions for key generation and secure protocol purposes. A one-way hash function is integrated to ensure provable security against modeling attacks, while a lookaside buffer accelerates batch sampling and minimizes reliance on error correction codes. Implemented on the Genesys 2 FPGA, RISecure-PUF improves at least $2.72\times$ in batch scenarios with negligible hardware overhead and a maximum performance reduction of $10.7\%$, enabled by reusing the hash function module in integrated environments such as cryptographic engines.

cs.CR↗

arXiv2024

Witnessing Quantum Incompatibility Structures in High-Dimensional Multimeasurement Systems

Quantum incompatibility, referred as the phenomenon that some quantum measurements cannot be performed simultaneously, is necessary for various quantum information processing tasks, such as nonlocality and steering. When these applications come to high-dimensional multimeasurement scenarios, it is crucial and challenging to witness the incompatibility of measurements with complex structures. To address this problem, we propose a modified quantum state discrimination protocol that decomposes complex compatibility structures into pairwise ones and employs noise robustness to bound incompatibility structures. We then derive arithmetic bounds for arbitrary measurements and analytical bounds for mutually unbiased bases, and capture some quantum incompatibility structures where measurements are partly compatible and partly incompatible. Finally, we experimentally demonstrate our results and connect them with quantum steering, quantum simulability and quantum communications.

quant-ph↗

arXiv2024

Fast Extrinsic Calibration for Multiple Inertial Measurement Units in Visual-Inertial System

In this paper, we propose a fast extrinsic calibration method for fusing multiple inertial measurement units (MIMU) to improve visual-inertial odometry (VIO) localization accuracy. Currently, data fusion algorithms for MIMU highly depend on the number of inertial sensors. Based on the assumption that extrinsic parameters between inertial sensors are perfectly calibrated, the fusion algorithm provides better localization accuracy with more IMUs, while neglecting the effect of extrinsic calibration error. Our method builds two non-linear least-squares problems to estimate the MIMU relative position and orientation separately, independent of external sensors and inertial noises online estimation. Then we give the general form of the virtual IMU (VIMU) method and propose its propagation on manifold. We perform our method on datasets, our self-made sensor board, and board with different IMUs, validating the superiority of our method over competing methods concerning speed, accuracy, and robustness. In the simulation experiment, we show that only fusing two IMUs with our calibration method to predict motion can rival nine IMUs. Real-world experiments demonstrate better localization accuracy of the VIO integrated with our calibration method and VIMU propagation on manifold.

cs.RO↗

arXiv2024

Teamwork Makes TEE Work: Open and Resilient Remote Attestation on Decentralized Trust

Remote Attestation (RA) enables the integrity and authenticity of applications in Trusted Execution Environment (TEE) to be verified. Existing TEE RA designs employ a centralized trust model where they rely on a single provisioned secret key and a centralized verifier to establish trust for remote parties. This model is however brittle and can be untrusted under advanced attacks nowadays. Besides, most designs only have fixed procedures once deployed, making them hard to adapt to different emerging situations and provide resilient functionalities. Therefore, we propose JANUS, an open and resilient TEE RA scheme. To decentralize trust, we, on one hand, introduce Physically Unclonable Function (PUF) as an intrinsic root of trust (RoT) in TEE to directly provide physical trusted measurements. On the other hand, we design novel decentralized verification functions on smart contract with result audits and RA session snapshot. Furthermore, we design an automated switch mechanism that allows JANUS to remain resilient and offer flexible RA services under various situations. We provide a UC-based security proof and demonstrate the scalability and generality of JANUS by implementing an complete prototype.

cs.CR↗

arXiv2024

Structure-Aware Consensus Network on Graphs with Few Labeled Nodes

Graph node classification with few labeled nodes presents significant challenges due to limited supervision. Conventional methods often exploit the graph in a transductive learning manner. They fail to effectively utilize the abundant unlabeled data and the structural information inherent in graphs. To address these issues, we introduce a Structure-Aware Consensus Network (SACN) from three perspectives. Firstly, SACN leverages a novel structure-aware consensus learning strategy between two strongly augmented views. The proposed strategy can fully exploit the potentially useful information of the unlabeled nodes and the structural information of the entire graph. Secondly, SACN uniquely integrates the graph's structural information to achieve strong-to-strong consensus learning, improving the utilization of unlabeled data while maintaining multiview learning. Thirdly, unlike two-branch graph neural network-based methods, SACN is designed for multiview feature learning within a single-branch architecture. Furthermore, a class-aware pseudolabel selection strategy helps address class imbalance and achieve effective weak-to-strong supervision. Extensive experiments on three benchmark datasets demonstrate SACN's superior performance in node classification tasks, particularly at very low label rates, outperforming state-of-the-art methods while maintaining computational simplicity.The source code is available at https://github.com/kunzhan/SACN

cs.LG↗