Author name cluster

Fei Hou

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8 papers

1 author row

AAAI Conference 2026 Conference Paper

IntentMotion: Learning Intent-Aware Human Motion from Language in 3D Scenes

Wenfeng Song
Shi Zheng
Xinyu Zhang
Xingliang Jin
Aimin Hao
Fei Hou
Xia Hou
Shuai Li

Generating human motion in complex 3D scenes from text is a challenging task with broad applications. However, existing methods often overlook realistic physical contact, resulting in visually plausible but physically unrealistic motion, e.g., penetration. To alleviate this, we propose IntentMotion, a novel framework that generates human motion in 3D scenes from natural language instructions by explicitly modeling intent. We first introduce the Intention-Guided Contact Field (IGCF). This differentiable voxel-based contact region representation explicitly aligns parsed language roles with spatial contact regions through a hierarchical attention mechanism. IGCF is jointly trained with a diffusion-based motion generator, allowing contact predictions to adapt dynamically through gradient feedback. To improve the controllability and physics-aware motion, we further propose an Intention-Aware Diffusion Model (IADM), which decouples the high-level semantic planning from the low-level contact refinement in a coarse-to-fine process. The optimized contact cues are utilized to guide the synthesis of a coarse trajectory, followed by refining detailed pose sequences under IGCF supervision. Experiments on the HUMANISE and LINGO datasets demonstrate that our IntentMotion outperforms recent baselines in contact accuracy, semantic alignment, and generalization to unseen scenes.

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AAAI Conference 2025 Conference Paper

CtrlAvatar: Controllable Avatars Generation via Disentangled Invertible Networks

Wenfeng Song
Yang Ding
Fei Hou
Shuai Li
Aimin Hao
Xia Hou

As virtual experiences grow in popularity, the demand for realistic, personalized, and animatable human avatars increases. Traditional methods, relying on fixed templates, often produce costly avatars that lack expressiveness and realism. To overcome these challenges, we introduce Controllable Avatars generation via disentangled invertible networks (CtrlAvatar), a real-time framework for generating lifelike and customizable avatars. CtrlAvatar uses disentangled invertible networks to separate the deformation process into implicit body geometry and explicit texture components. This approach eliminates the need for repeated occupancy reconstruction, enabling detailed and coherent animations. The body geometry component ensures anatomical accuracy, while the texture component allows for complex, artifact-free clothing customization. This architecture ensures smooth integration between body movements and surface details. By optimizing transformations with position-varying offsets from the avatar’s initial Linear Blend Skinning vertices, CtrlAvatar achieves flexible, natural deformations that adapt to various scenarios. Extensive experiments show that CtrlAvatar outperforms other methods in quality, diversity, controllability, and cost-efficiency, marking a significant advancement in avatar generation.

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AAAI Conference 2025 Conference Paper

Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction

Cheng Xu
Fei Hou
Wencheng Wang
Hong Qin
Zhebin Zhang
Ying He

While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite their flexibility, UDFs encounter significant challenges in high-fidelity 3D reconstruction, such as non-differentiability at the zero level set, difficulty in achieving the exact zero value, numerous local minima, vanishing gradients, and oscillating gradient directions near the zero level set. To address these challenges, we propose Details Enhanced UDF (DEUDF) learning that integrates normal alignment and the SIREN network for capturing fine geometric details, adaptively weighted Eikonal constraints to address vanishing gradients near the target surface, unconditioned MLP-based UDF representation to relax non-negativity constraints, and DCUDF for extracting the local minimal average distance surface. These strategies collectively stabilize the learning process from unoriented point clouds and enhance the accuracy of UDFs. Our computational results demonstrate that DEUDF outperforms existing UDF learning methods in both accuracy and the quality of reconstructed surfaces.

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NeurIPS Conference 2025 Conference Paper

MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstruction

Xuhui Chen
Fei Hou
Wencheng Wang
Hong Qin
Ying He

Unsigned distance fields (UDFs) are widely used in 3D deep learning due to their ability to represent shapes with arbitrary topology. While prior work has largely focused on learning UDFs from point clouds or multi-view images, extracting meshes from UDFs remains challenging, as the learned fields rarely attain exact zero distances. A common workaround is to reconstruct signed distance fields (SDFs) locally from UDFs to enable surface extraction via Marching Cubes. However, this often introduces topological artifacts such as holes or spurious components. Moreover, local SDFs are inherently incapable of representing non-manifold geometry, leading to complete failure in such cases. To address this gap, we propose MIND ($\mathrm{\underline{M}aterial}$ $\mathrm{\underline{I}nterface}$ $\mathrm{from}$ $\mathrm{\underline{N}on}$-$\mathrm{manifold}$ $\mathrm{\underline{D}istance}$ $\mathrm{fields}$), a novel algorithm for generating material interfaces directly from UDFs, enabling non-manifold mesh extraction from a global perspective. The core of our method lies in deriving a meaningful spatial partitioning from the UDF, where the target surface emerges as the interface between distinct regions. We begin by computing a two-signed local field to distinguish the two sides of manifold patches, and then extend this to a multi-labeled global field capable of separating all sides of a non-manifold structure. By combining this multi-labeled field with the input UDF, we construct material interfaces that support non-manifold mesh extraction via a multi-labeled Marching Cubes algorithm. Extensive experiments on UDFs generated from diverse data sources, including point cloud reconstruction, multi-view reconstruction, and medial axis transforms, demonstrate that our approach robustly handles complex non-manifold surfaces and significantly outperforms existing methods. The source code is available at https: //github. com/jjjkkyz/MIND.

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NeurIPS Conference 2024 Conference Paper

From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $\alpha$-NeuS

Haoran Zhang
Junkai Deng
Xuhui Chen
Fei Hou
Wencheng Wang
Hong Qin
Chen Qian
Ying He

Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. Recent advances in neural radiance fields and its variants primarily address opaque or transparent objects, encountering difficulties to reconstruct both transparent and opaque objects simultaneously. This paper introduces $\alpha$-NeuS$\textemdash$an extension of NeuS$\textemdash$that proves NeuS is unbiased for materials from fully transparent to fully opaque. We find that transparent and opaque surfaces align with the non-negative local minima and the zero iso-surface, respectively, in the learned distance field of NeuS. Traditional iso-surfacing extraction algorithms, such as marching cubes, which rely on fixed iso-values, are ill-suited for such data. We develop a method to extract the transparent and opaque surface simultaneously based on DCUDF. To validate our approach, we construct a benchmark that includes both real-world and synthetic scenes, demonstrating its practical utility and effectiveness. Our data and code are publicly available at https: //github. com/728388808/alpha-NeuS.

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IJCAI Conference 2023 Conference Paper

Image Composition with Depth Registration

Zan Li
Wencheng Wang
Fei Hou

Handling occlusions is still a challenging problem for image composition. It always requires the source contents to be completely in front of the target contents or needs manual interventions to adjust occlusions, which is very tedious. Though several methods have suggested exploiting priors or learning techniques for promoting occlusion determination, their potentials are much limited. This paper addresses the challenge by presenting a depth registration method for merging the source contents seamlessly into the 3D space that the target image represents. Thus, the occlusions between the source contents and target contents can be conveniently handled through pixel-wise depth comparisons, allowing the user to more efficiently focus on the designs for image composition. Experimental results show that we can conveniently handle occlusions in image composition and improve efficiency by about 4 times compared to Photoshop.

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JBHI Journal 2019 Journal Article

A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation

Pranjal Sahu
Dantong Yu
Mallesham Dasari
Fei Hou
Hong Qin

The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous models that proposed computationally intensive deep ensemble models or three-dimensional CNN models, we propose a lightweight, multiple view sampling based multi-section CNN architecture. The model obtains a nodule's cross sections from multiple view angles and encodes the nodule's volumetric information into a compact representation by aggregating information from its different cross sections via a view pooling layer. The compact feature is subsequently used for the task of nodule classification. The method does not require the nodule's spatial annotation and works directly on the cross sections generated from volume enclosing the nodule. We evaluated the proposed method on lung image database consortium (LIDC) and image database resource initiative (IDRI) dataset. It achieved the state-of-the-art performance with a mean 93. 18% classification accuracy. The architecture could also be used to select the representative cross sections determining the nodule's malignancy that facilitates in the interpretation of results. Because of being lightweight, the model could be ported to mobile devices, which brings the power of artificial intelligence (AI) driven application directly into the practitioner's hand.

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JBHI Journal 2016 Journal Article

Robust Optimization-Based Coronary Artery Labeling From X-Ray Angiograms

Xinglong Liu
Fei Hou
Hong Qin
Aimin Hao

In this paper, we present an efficient robust labeling method for coronary arteries from X-ray angiograms based on energy optimization. The fundamental goal of this research is to facilitate the analysis and diagnosis of interventional surgery in the most efficient way, and such effort could also improve the performance during doctor training, and surgery simulation and planning. Compared to the prior state-of-the-art, our method is much more robust to resist noises and is tolerant to even incomplete data because of the “ built-in ” nature of global optimization. We start with a fully parallelized algorithm based on Hessian matrix to extract the tubular structure from the X-ray angiograms as vessel candidates. Then, instead of using the candidates directly, we use the grow cut (Vezhnevets and V. Konouchine, Growcut: Interactive multi-label N-D image segmentation by cellular automata, in Proc. of Graphicon, 2005, pp. 150–156.) method, which is similar to graph cut (Boykov et al. , Fast approximate energy minimization via graph cuts, IEEE Trans. Pattern Anal. Mach. Intell. , vol. 23, no. 11, pp. 1222–1239, Nov. 2001.)but with better performance to extract the precise vessel structure from the images. Next, we use the fast marching method with second derivatives and cross neighbors to extract the accurate skeleton segments. After that, we propose an efficient method based on iterative closest point (Z. Zhang, Iterative point matching for registration of free-form curves and surfaces, Int J. Comput. Vis. , vol. 13, no. 2, pp. 119–152, 1994.) to organize the skeleton segments by treating the continuity and similarity as extra constraints. Finally, we formulate the vessel labeling problem as an energy optimization problem and solve it using belief propagation. We also demonstrate several typical applications including flow velocity estimation, heart beat estimation, and vessel diameter estimation to show its practical uses in clinical diagnosis and treatment. Our experiments exhibit the correctness and robustness, as well as the high performance of our algorithm. We envision that our system would be of high utility for diagnosis and therapy to treat vessel-related diseases in a clinical setting in the near future.

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