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Jiahao Lu

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10 papers

2 author rows

TMLR Journal 2026 Journal Article

MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation

Longtao Zheng
Yifan Zhang
Hanzhong Guo
Jiachun Pan
Zhenxiong Tan
Jiahao Lu
Chuanxin Tang
Bo An

Recent advances in video diffusion models have unlocked new potential for realistic audio-driven talking video generation. However, maintaining long-term identity consistency, achieving seamless lip-audio synchronization, and producing natural, audio-aligned expressions in generated talking videos remain significant challenges. To address these challenges, we propose Memory-guided EMOtion-aware diffusion (MEMO), an end-to-end audio-driven portrait animation approach to generate identity-consistent and expressive talking videos. Our approach is built around two key modules: (1) a memory-guided temporal module, which enhances long-term identity consistency and motion smoothness by developing causal motion memory to store information from an extended past context to guide temporal modeling; and (2) an emotion-aware audio module, which replaces traditional cross attention with multi-modal attention to enhance audio-video interaction, while detecting emotions from audio to refine facial expressions via emotion-adaptive layer norm. Extensive quantitative and qualitative results demonstrate that MEMO generates more realistic talking videos across diverse image and audio types, outperforming state-of-the-art methods in overall quality, lip-audio synchronization, identity consistency, and expression-audio alignment. Our model and video demos are available at https://memoavatar.github.io.

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AAAI Conference 2026 Conference Paper

MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting

Hanzhi Chang
Ruijie Zhu
Wenjie Chang
Mulin Yu
Yanzhe Liang
Jiahao Lu
Zhuoyuan Li
Tianzhu Zhang

Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks.

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AAAI Conference 2025 Conference Paper

Adaptive Siamese Masked Autoencoder with Global Optimization for Unsupervised Point Cloud Shape Correspondence

Jiacheng Deng
Jiahao Lu

Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between point clouds without annotated data. Ensuring efficiency and accuracy is crucial for practically implementing point cloud shape correspondence. Although the current methods have achieved desirable performance, the nature of encoding at dense points limits their application in actual scenarios. Moreover, independently computing per-point correspondences results in numerous multiple-to-one erroneous correspondences. To address these issues, we present an Adaptive siamese Masked autoencoder with Global Optimization (AMIGO), comprising a siamese masked autoencoder and a global optimization module. In the siamese masked autoencoder, we downsample the input point cloud and employ adaptive siamese mask operations to boost the coding capabilities of the encoder, thereby mitigating the information loss caused by downsampling. In the global optimization module, optimal transport is only utilized to generate pseudo-labels during the training phase, facilitating the efficient global planning of the correspondence results. Extensive experiments on four standard human and animal benchmarks demonstrate that AMIGO surpasses existing methods with remarkable margins, achieving new state-of-the-art results.

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AAAI Conference 2025 Conference Paper

DiffCorr: Conditional Diffusion Model with Reliable Pseudo-Label Guidance for Unsupervised Point Cloud Shape Correspondence

Jiacheng Deng
Jiahao Lu
Zhixin Cheng
Wenfei Yang

Unsupervised point cloud shape correspondence aims to establish dense correspondences between source and target point clouds. Existing methods universally follow a one-step paradigm to obtain shape correspondence directly, but it often fails in large-scale motions of humans and animals. To address this challenge, we propose a conditional Diffusion model with reliable pseudo-label guidance for unsupervised point cloud shape Correspondence (DiffCorr), including a transformer-based conditional diffusion model and a reliable pseudo-label generator. The proposed DiffCorr enjoys several merits. Firstly, the transformer-based conditional diffusion model implements a coarse-to-fine optimization for coarse correspondences. Secondly, we design a reliable pseudo-label generator to provide high-quality pseudo-labels for training. Extensive experiments on four human and animal datasets demonstrate that DiffCorr surpasses state-of-the-art methods and exhibits favorable generalization capabilities.

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AAAI Conference 2025 Conference Paper

Pamba: Enhancing Global Interaction in Point Clouds via State Space Model

Zhuoyuan Li
Yubo Ai
Jiahao Lu
ChuXin Wang
Jiacheng Deng
Hanzhi Chang
Yanzhe Liang
Wenfei Yang

Transformers have demonstrated impressive results for 3D point cloud semantic segmentation. However, the quadratic complexity of transformer makes computation costs high, limiting the number of points that can be processed simultaneously and impeding the modeling of long-range dependencies between objects in a single scene. Drawing inspiration from the great potential of recent state space models (SSM) for long sequence modeling, we introduce Mamba, an SSM-based architecture, to the point cloud domain and propose Pamba, a novel architecture with strong global modeling capability under linear complexity. Specifically, to make the disorderness of point clouds fit in with the causal nature of Mamba, we propose a multi-path serialization strategy applicable to point clouds. Besides, we propose the ConvMamba block to compensate for the shortcomings of Mamba in modeling local geometries and in unidirectional modeling. Pamba obtains state-of-the-art results on several 3D point cloud segmentation tasks, including ScanNet v2, ScanNet200, S3DIS and nuScenes, while its effectiveness is validated by extensive experiments.

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ICLR Conference 2025 Conference Paper

Poison-splat: Computation Cost Attack on 3D Gaussian Splatting

Jiahao Lu
Yifan Zhang 0004
Qiuhong Shen
Xinchao Wang
Shuicheng Yan

3D Gaussian splatting (3DGS), known for its groundbreaking performance and efficiency, has become a dominant 3D representation and brought progress to many 3D vision tasks. However, in this work, we reveal a significant security vulnerability that has been largely overlooked in 3DGS: the computation cost of training 3DGS could be maliciously tampered by poisoning the input data. By developing an attack named Poison-splat, we reveal a novel attack surface where the adversary can poison the input images to drastically increase the computation memory and time needed for 3DGS training, pushing the algorithm towards its worst computation complexity. In extreme cases, the attack can even consume all allocable memory, leading to a Denial-of-Service (DoS) that disrupts servers, resulting in practical damages to real-world 3DGS service vendors. Such a computation cost attack is achieved by addressing a bi-level optimization problem through three tailored strategies: attack objective approximation, proxy model rendering, and optional constrained optimization. These strategies not only ensure the effectiveness of our attack but also make it difficult to defend with simple defensive measures. We hope the revelation of this novel attack surface can spark attention to this crucial yet overlooked vulnerability of 3DGS systems. Our code is available at https://github.com/jiahaolu97/poison-splat .

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NeurIPS Conference 2025 Conference Paper

TrackingWorld: World-centric Monocular 3D Tracking of Almost All Pixels

Jiahao Lu
Weitao Xiong
Jiacheng Deng
Peng Li
Tianyu Huang
Zhiyang Dou
Cheng Lin
Sai-Kit Yeung

Monocular 3D tracking aims to capture the long-term motion of pixels in 3D space from a single monocular video and has witnessed rapid progress in recent years. However, we argue that the existing monocular 3D tracking methods still fall short in separating the camera motion from foreground dynamic motion and cannot densely track newly emerging dynamic subjects in the videos. To address these two limitations, we propose TrackingWorld, a novel pipeline for dense 3D tracking of almost all pixels within a world-centric 3D coordinate system. First, we introduce a tracking upsampler that efficiently lifts the arbitrary sparse 2D tracks into dense 2D tracks. Then, to generalize the current tracking methods to newly emerging objects, we apply the upsampler to all frames and reduce the redundancy of 2D tracks by eliminating the tracks in overlapped regions. Finally, we present an efficient optimization-based framework to back-project dense 2D tracks into world-centric 3D trajectories by estimating the camera poses and the 3D coordinates of these 2D tracks. Extensive evaluations on both synthetic and real-world datasets demonstrate that our system achieves accurate and dense 3D tracking in a world-centric coordinate frame.

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NeurIPS Conference 2024 Conference Paper

DN-4DGS: Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Rendering

Jiahao Lu
Jiacheng Deng
Ruijie Zhu
Yanzhe Liang
Wenfei Yang
Tianzhu Zhang
Xu Zhou

Dynamic scenes rendering is an intriguing yet challenging problem. Although current methods based on NeRF have achieved satisfactory performance, they still can not reach real-time levels. Recently, 3D Gaussian Splatting (3DGS) has garnered researchers' attention due to their outstanding rendering quality and real-time speed. Therefore, a new paradigm has been proposed: defining a canonical 3D gaussians and deforming it to individual frames in deformable fields. However, since the coordinates of canonical 3D gaussians are filled with noise, which can transfer noise into the deformable fields, and there is currently no method that adequately considers the aggregation of 4D information. Therefore, we propose Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Rendering (DN-4DGS). Specifically, a Noise Suppression Strategy is introduced to change the distribution of the coordinates of the canonical 3D gaussians and suppress noise. Additionally, a Decoupled Temporal-Spatial Aggregation Module is designed to aggregate information from adjacent points and frames. Extensive experiments on various real-world datasets demonstrate that our method achieves state-of-the-art rendering quality under a real-time level. Code is available at https: //github. com/peoplelu/DN-4DGS.

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NeurIPS Conference 2024 Conference Paper

MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting

Ruijie Zhu
Yanzhe Liang
Hanzhi Chang
Jiacheng Deng
Jiahao Lu
Wenfei Yang
Tianzhu Zhang
Yongdong Zhang

Dynamic scene reconstruction is a long-term challenge in the field of 3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights into this problem. Although subsequent efforts rapidly extend static 3D Gaussian to dynamic scenes, they often lack explicit constraints on object motion, leading to optimization difficulties and performance degradation. To address the above issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS, which explores explicit motion priors to guide the deformation of 3D Gaussians. Specifically, we first introduce an optical flow decoupling module that decouples optical flow into camera flow and motion flow, corresponding to camera movement and object motion respectively. Then the motion flow can effectively constrain the deformation of 3D Gaussians, thus simulating the motion of dynamic objects. Additionally, a camera pose refinement module is proposed to alternately optimize 3D Gaussians and camera poses, mitigating the impact of inaccurate camera poses. Extensive experiments in the monocular dynamic scenes validate that MotionGS surpasses state-of-the-art methods and exhibits significant superiority in both qualitative and quantitative results. Project page: https: //ruijiezhu94. github. io/MotionGS_page.

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NeurIPS Conference 2020 Conference Paper

CoMIR: Contrastive Multimodal Image Representation for Registration

Nicolas Pielawski
Elisabeth Wetzer
Johan Öfverstedt
Jiahao Lu
Carolina Wählby
Joakim Lindblad
Natasa Sladoje

We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations). CoMIRs enable the registration of multimodal images where existing registration methods often fail due to a lack of sufficiently similar image structures. CoMIRs reduce the multimodal registration problem to a monomodal one, in which general intensity-based, as well as feature-based, registration algorithms can be applied. The method involves training one neural network per modality on aligned images, using a contrastive loss based on noise-contrastive estimation (InfoNCE). Unlike other contrastive coding methods, used for, e. g. , classification, our approach generates image-like representations that contain the information shared between modalities. We introduce a novel, hyperparameter-free modification to InfoNCE, to enforce rotational equivariance of the learnt representations, a property essential to the registration task. We assess the extent of achieved rotational equivariance and the stability of the representations with respect to weight initialization, training set, and hyperparameter settings, on a remote sensing dataset of RGB and near-infrared images. We evaluate the learnt representations through registration of a biomedical dataset of bright-field and second-harmonic generation microscopy images; two modalities with very little apparent correlation. The proposed approach based on CoMIRs significantly outperforms registration of representations created by GAN-based image-to-image translation, as well as a state-of-the-art, application-specific method which takes additional knowledge about the data into account. Code is available at: https: //github. com/MIDA-group/CoMIR.

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