arXiv subjects

Zhihao Huang

Publications and source records attributed to Zhihao Huang.

6 recordsLinked to original sources

arXiv2026

ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Discrete Diffusion Models

Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by continuous approaches. We introduce ViewMask-1-to-3, formulating multi-view generation as a discrete sequence modeling problem where each viewpoint is represented as visual tokens from MAGVIT-v2. Through discrete diffusion via masked token prediction, our approach enables progressive multi-view generation via iterative token unmasking, unifying language and vision in a shared token space. Importantly, simple random masking combined with self-attention naturally encourages cross-view consistency without specialized architectures or 3D geometric priors. Our method outperforms the baseline on the GSO and 3D-FUTURE benchmarks, ranking first on average across standard image metrics, and achieving a 10.6% higher IoU than continuous diffusion models on 3D-FUTURE. Furthermore, the proposed framework can be naturally extended to support text-to-image generation and multimodal understanding, highlighting its potential toward a more unified paradigm for multimodal understanding and generation.

cs.CV↗

arXiv2026

COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents

LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmful intents may decompose into seemingly innocuous sub-queries that lead to unsafe outcomes. Existing alignment methods struggle to capture sparse safety signals and fail to supervise diverse violations across multi-step interactions. We propose COMPASS, a Cognitive MCTS-Guided Process Alignment framework designed to achieve robust safety alignment throughout the agent workflow while preserving general utility. COMPASS integrates cognitive tree exploration (CTE) to efficiently synthesize stealthy attack trajectories, and introspective step-wise alignment (ISA) to isolate risky intermediate actions for fine-grained process supervision. Empirical results show that COMPASS achieves a favorable safety-utility trade-off while requiring substantially less training data.

cs.AI↗

arXiv2026

Towards Seamless Interaction: Causal Turn-Level Modeling of Interactive 3D Conversational Head Dynamics

Human conversation involves continuous exchanges of speech and nonverbal cues such as head nods, gaze shifts, and facial expressions that convey attention and emotion. Modeling these bidirectional dynamics in 3D is essential for building expressive avatars and interactive robots. However, existing frameworks often treat talking and listening as independent processes or rely on non-causal full-sequence modeling, hindering temporal coherence across turns. We present TIMAR (Turn-level Interleaved Masked AutoRegression), a causal framework for 3D conversational head generation that models dialogue as interleaved audio-visual contexts. It fuses multimodal information within each turn and applies turn-level causal attention to accumulate conversational history, while a lightweight diffusion head predicts continuous 3D head dynamics that captures both coordination and expressive variability. Experiments on the DualTalk benchmark show that TIMAR reduces Fréchet Distance and MSE by 15-30% on the test set, and achieves similar gains on out-of-distribution data. The source code has been released at https://github.com/CoderChen01/towards-seamless-interaction.

cs.CV↗

arXiv2025

NFIG: Multi-Scale Autoregressive Image Generation via Frequency Ordering

Autoregressive models have achieved significant success in image generation. However, unlike the inherent hierarchical structure of image information in the spectral domain, standard autoregressive methods typically generate pixels sequentially in a fixed spatial order. To better leverage this spectral hierarchy, we introduce NextFrequency Image Generation (NFIG). NFIG is a novel framework that decomposes the image generation process into multiple frequency-guided stages. NFIG aligns the generation process with the natural image structure. It does this by first generating low-frequency components, which efficiently capture global structure with significantly fewer tokens, and then progressively adding higher-frequency details. This frequency-aware paradigm offers substantial advantages: it not only improves the quality of generated images but crucially reduces inference cost by efficiently establishing global structure early on. Extensive experiments on the ImageNet-256 benchmark validate NFIG's effectiveness, demonstrating superior performance (FID: 2.81) and a notable 1.25x speedup compared to the strong baseline VAR-d20. The source code is available at https://github.com/Pride-Huang/NFIG.

cs.CV↗

arXiv2024

VDPI: Video Deblurring with Pseudo-inverse Modeling

Video deblurring is a challenging task that aims to recover sharp sequences from blur and noisy observations. The image-formation model plays a crucial role in traditional model-based methods, constraining the possible solutions. However, this is only the case for some deep learning-based methods. Despite deep-learning models achieving better results, traditional model-based methods remain widely popular due to their flexibility. An increasing number of scholars combine the two to achieve better deblurring performance. This paper proposes introducing knowledge of the image-formation model into a deep learning network by using the pseudo-inverse of the blur. We use a deep network to fit the blurring and estimate pseudo-inverse. Then, we use this estimation, combined with a variational deep-learning network, to deblur the video sequence. Notably, our experimental results demonstrate that such modifications can significantly improve the performance of deep learning models for video deblurring. Furthermore, our experiments on different datasets achieved notable performance improvements, proving that our proposed method can generalize to different scenarios and cameras.

cs.CV↗

arXiv2020

Preparation of the AlTiNiCuCox system high-entropy alloys and structural analysis

This study aimed to explore and develop a new material with high cost-effectiveness, excellent strength, light weight, high hardness, great wear resistance, corrosion resistance, and favorable oxidation resistance. Structural analysis suggested that, with the change in Co addition amount, the surface morphology and structure of the alloy system changed. XRD analysis indicated that, the alloy system was the FCC+BCC mixed structure. In addition, metallographical demonstrated that, with the increase in Co content, the dendritic crystal transformed from big block to dendritic structure, then to snowflake, gradually to petal-like, and finally to petal shape. SEM-EDS analysis revealed that, Cu element was enriched in interdendritic site, while Ti, Ni, Al and Co elements were enriched in dendrite. Besides, TEM and TEM-EDS analysis indicated that, there was nano-size precipitate of small particles in the Cu-enriched block region, along with dislocation; further, there was twin structure inside the dendrite, as well as the second phase with different morphology, and the second phase showed coherency with the matrix. The above analysis suggested that, the intercrystalline structure was the Cu-enriched phase of FCC structure; the internal matrix of grain was the NiTi and TiCo phases of BCC structure; and the second phases inside the grain were the AlCu2Ti,AlNi2Ti,AlCo2Ti and CuNi phases of FCC structure. Taken together, the AlTiNiCuCox system novel alloys have changed phase structures and phase types of the alloy system.

physics.app-ph↗