Zonglin Li

AAAI Conference 2026 Conference Paper

DialoGen: Towards Dialog Gesture Generation via Identity-Decoupled Style Guidance in Interactive Diffusion Model

• Weiyu Zhao
• Chenyang Wang
• Liangxiao Hu
• Zonglin Li
• Wei Yu
• Shengping Zhang

We propose DialoGen, a novel framework for generating realistic gestures for both interlocutors in dialog scenarios, conditioned on conversational audios. Unlike most existing methods that focus solely on a single speaker, DialoGen simultaneously generates synchronized gestures for both participants while also embedding identity-decoupled style into generated gestures that enhance realism and expressiveness. To ensure precise synchronization between interlocutors, DialoGen adopts an interactive dual-diffusion model with mutual interaction estimation, which integrates interaction correlation into the diffusion process. More importantly, by leveraging supervised contrastive learning, we develop the identity-decoupled style guidance to adaptively decompose the identity-specific style of interlocutors into latent space, enabling multi-style dialog gesture generation. Extensive experimental results demonstrate that our model significantly outperforms existing methods in generating realistic, speech-aligned, identity-specific gestures, offering a high-quality solution for various dialog scenarios.

NeurIPS Conference 2025 Conference Paper

BeliefMapNav: 3D Voxel-Based Belief Map for Zero-Shot Object Navigation

• Zibo Zhou
• Yue Hu
• Lingkai Zhang
• Zonglin Li
• Siheng Chen

Zero-shot object navigation (ZSON) allows robots to find target objects in unfamiliar environments using natural language instructions, without relying on pre-built maps or task-specific training. Recent general-purpose models, such as large language models (LLMs) and vision-language models (VLMs), equip agents with semantic reasoning abilities to estimate target object locations in a zero-shot manner. However, these models often greedily select the next goal without maintaining a global understanding of the environment and are fundamentally limited in the spatial reasoning necessary for effective navigation. To overcome these limitations, we propose a novel 3D voxel-based belief map that estimates the target’s prior presence distribution within a voxelized 3D space. This approach enables agents to integrate semantic priors from LLMs and visual embeddings with hierarchical spatial structure, alongside real-time observations, to build a comprehensive 3D global posterior belief of the target’s location. Building on this 3D voxel map, we introduce BeliefMapNav, an efficient navigation system with two key advantages: i) grounding LLM semantic reasoning within the 3D hierarchical semantics voxel space for precise target position estimation, and ii) integrating sequential path planning to enable efficient global navigation decisions. Experiments on HM3D and HSSD benchmarks show that BeliefMapNav achieves state-of-the-art (SOTA) Success Rate (SR) and Success weighted by Path Length (SPL), with a notable 9. 7 SPL improvement over the previous best SR method, validating its effectiveness and efficiency.

AAAI Conference 2025 Conference Paper

Multi-view Consistent 3D Panoptic Scene Understanding

• Xianzhu Liu
• Xin Sun
• Haozhe Xie
• Zonglin Li
• Ru Li
• Shengping Zhang

3D panoptic scene understanding seeks to create novel view images with 3D-consistent panoptic segmentation, which is crucial for many vision and robotics applications. Mainstream methods (e.g., Panoptic Lifting) directly use machine-generated 2D panoptic segmentation masks as training labels. However, these generated masks often exhibit multi-view inconsistencies, leading to ambiguities during the optimization process. To address this, we present Multi-view Consistent 3D Panoptic Scene Understanding (MVC-PSU), featuring two key components: 1) Probabilistic Semantic Aligner, which associates semantic information of corresponding pixels across multiple views by probabilistic alignment to ensure that predicted panoptic segmentation masks are consistent across different views. 2) Geometric Consistency Enforcer, which uses multi-view projection and monocular depth consistency to ensure that the geometry of the reconstructed scene is accurate and consistent across different views. Experimental results demonstrate that the proposed MVC-PSU surpasses state-of-the-art methods on the ScanNet, Replica, and HyperSim datasets.

AAAI Conference 2025 Conference Paper

OTPNet: ODE-inspired Tuning-free Proximal Network for Remote Sensing Image Fusion

• Wei Yu
• Zonglin Li
• Qinglin Liu
• Xin Sun

Remote sensing image fusion aims to reconstruct a high spatial and spectral resolution image by integrating the spatial and spectral information from multiple remote sensing sensor data. Despite the remarkable progress of deep learning-based fusion methods, most existing methods rely on manual network architecture design and hyperparameter tuning, lacking sufficient interpretability and adaptability. To address this limitation, we propose a novel neural Ordinary Differential Equation (ODE)-inspired tuning-free proximal splitting algorithm, which splits remote sensing image fusion as two optimization problems regularized by deep priors to model the fusion of spatial and spectral. Firstly, based on the physical properties of spatial and spectral information, the two problems are optimized by two proximal splitting operators to iteratively integrate spatial-spectral complementary information, eliminating or suppressing redundant information to reduce fusion errors. Secondly, considering the efficiency of neural ODE in reducing optimization error, we utilize a high-order numerical scheme to customize the proximal operator theoretically without additional handcrafted design and parameter tuning. Finally, by incorporating the numerical scheme as a solver into the proximal optimization algorithm, we derive an ODE-inspired Tuning-free Proximal Network, dubbed OTPNet, which achieves efficient and robust fusion reconstruction. Extensive experiments on nine datasets across three different remote sensing image fusion tasks show that our OTPNet outperforms existing state-of-the-art approaches, which validates the effectiveness of our method.

AAAI Conference 2025 Conference Paper

Path-Adaptive Matting for Efficient Inference Under Various Computational Cost Constraints

• Qinglin Liu
• Zonglin Li
• Xiaoqian Lv
• Xin Sun
• Ru Li
• Shengping Zhang

In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network. Existing matting methods which have not explored scalable architectures or path-learning strategies, fail to tackle this challenge. To overcome these limitations, we introduce Path-Adaptive Matting (PAM), a framework that dynamically adjusts network paths based on image contexts and computational cost constraints. We formulate the training of the computational cost-constrained matting network as a bilevel optimization problem, jointly optimizing the matting network and the path estimator. Building on this formalization, we design a path-adaptive matting architecture by incorporating path selection layers and learnable connect layers to estimate optimal paths and perform efficient inference within a unified network. Furthermore, we propose a performance-aware path-learning strategy to generate path labels online by evaluating a few paths sampled from the prior distribution of optimal paths and network estimations, enabling robust and efficient online path learning. Experiments on five image matting datasets demonstrate that the proposed PAM framework achieves competitive performance across a range of computational cost constraints.

AAAI Conference 2025 Conference Paper

ProsodyTalker: 3D Visual Speech Animation via Prosody Decomposition

• Zonglin Li
• Xiaoqian Lv
• Qinglin Liu
• Quanling Meng
• Xin Sun
• Shengping Zhang

Most existing 3D visual speech animation methods synthesize lip movements synchronized with speech, which however neglect head poses and therefore degrade the animation realism. The animation of head poses presents two primary challenges: (1) the intricate mapping between speech and head poses remains poorly understood and (2) the absence of 4D face datasets featuring realistic head poses. Inspired by prosody decomposition in speech processing, we discern that head movements correlate with the fundamental frequency (F0) of speech prosody, while lip movements align with the language content. These observations motivate us to propose a novel framework, dubbed ProsodyTalker, that concurrently synthesizes lip and head movements, grounded in the principles of prosody decomposition. The core idea is first to adopt information perturbation to explicitly decompose the speech prosody into pose-related F0 and lip-related language content. Then, an autoregressive content-oriented fusion decoder is employed to enhance lip synchronization in the synthesized facial sequences. To synthesize head poses, we design a transformer-based variational autoencoder to learn a latent distribution of facial sequences and propose an F0-conditioned latent diffusion model to establish a probabilistic mapping from F0 to pose-related latent codes. Furthermore, we contribute a large-scale 4D face dataset containing bunches of variations in identities, head poses and facial motions. Extensive experiments show that our method achieves more realistic animation than state-of-the-art methods.

NeurIPS Conference 2024 Conference Paper

High-Resolution Image Harmonization with Adaptive-Interval Color Transformation

• Quanling Meng
• Qinglin Liu
• Zonglin Li
• Xiangyuan Lan
• Shengping Zhang
• Liqiang Nie

Existing high-resolution image harmonization methods typically rely on global color adjustments or the upsampling of parameter maps. However, these methods ignore local variations, leading to inharmonious appearances. To address this problem, we propose an Adaptive-Interval Color Transformation method (AICT), which predicts pixel-wise color transformations and adaptively adjusts the sampling interval to model local non-linearities of the color transformation at high resolution. Specifically, a parameter network is first designed to generate multiple position-dependent 3-dimensional lookup tables (3D LUTs), which use the color and position of each pixel to perform pixel-wise color transformations. Then, to enhance local variations adaptively, we separate a color transform into a cascade of sub-transformations using two 3D LUTs to achieve the non-uniform sampling intervals of the color transform. Finally, a global consistent weight learning method is proposed to predict an image-level weight for each color transform, utilizing global information to enhance the overall harmony. Extensive experiments demonstrate that our AICT achieves state-of-the-art performance with a lightweight architecture. The code is available at https: //github. com/aipixel/AICT.

ICML Conference 2024 Conference Paper

Revisiting Context Aggregation for Image Matting

• Qinglin Liu
• Xiaoqian Lv
• Quanling Meng
• Zonglin Li
• Xiangyuan Lan
• Shuo Yang 0006
• Shengping Zhang
• Liqiang Nie

Traditional studies emphasize the significance of context information in improving matting performance. Consequently, deep learning-based matting methods delve into designing pooling or affinity-based context aggregation modules to achieve superior results. However, these modules cannot well handle the context scale shift caused by the difference in image size during training and inference, resulting in matting performance degradation. In this paper, we revisit the context aggregation mechanisms of matting networks and find that a basic encoder-decoder network without any context aggregation modules can actually learn more universal context aggregation, thereby achieving higher matting performance compared to existing methods. Building on this insight, we present AEMatter, a matting network that is straightforward yet very effective. AEMatter adopts a Hybrid-Transformer backbone with appearance-enhanced axis-wise learning (AEAL) blocks to build a basic network with strong context aggregation learning capability. Furthermore, AEMatter leverages a large image training strategy to assist the network in learning context aggregation from data. Extensive experiments on five popular matting datasets demonstrate that the proposed AEMatter outperforms state-of-the-art matting methods by a large margin. The source code is available at https: //github. com/aipixel/AEMatter.

EAAI Journal 2024 Journal Article

Selection of the structural severest design ground motions based on big data and random forest

• Xiaohong Long
• Chunde Lu
• Xiaopeng Gu
• Yongtao Ma
• Zonglin Li

NeurIPS Conference 2023 Conference Paper

ResMem: Learn what you can and memorize the rest

• Zitong Yang
• Michal Lukasik
• Vaishnavh Nagarajan
• Zonglin Li
• Ankit Rawat
• Manzil Zaheer
• Aditya K. Menon
• Sanjiv Kumar

The impressive generalization performance of modern neural networks is attributed in part to their ability to implicitly memorize complex training patterns. Inspired by this, we explore a novel mechanism to improve model generalization via explicit memorization. Specifically, we propose the residual-memorization (ResMem) algorithm, a new method that augments an existing prediction model (e. g. , a neural network) by fitting the model's residuals with a nearest-neighbor based regressor. The final prediction is then the sum of the original model and the fitted residual regressor. By construction, ResMem can explicitly memorize the training labels. We start by formulating a stylized linear regression problem and rigorously show that ResMem results in a more favorable test risk over a base linear neural network. Then, we empirically show that ResMem consistently improves the test set generalization of the original prediction model across standard vision and natural language processing benchmarks.

ICLR Conference 2023 Conference Paper

The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers

• Zonglin Li
• Chong You
• Srinadh Bhojanapalli
• Daliang Li
• Ankit Singh Rawat
• Sashank J. Reddi
• Ke Ye
• Felix Chern

This paper studies a curious phenomenon that machine learning model with Transformer architectures have sparse activation maps. By activation map we refer to the intermediate output of the multi-layer perceptrons (MLPs) after a ReLU activation function, and by "sparse" we mean that on average very few entries (e.g., 3.0% for T5-Base and 6.3% for ViT-B16) are nonzero for each input to MLP. Moreover, larger Transformers with more layers and wider MLP hidden dimensions are sparser as measured by the percentage of nonzero entries. Through extensive experiments we demonstrate that the emergence of sparsity is a prevalent phenomenon that occurs for both natural language processing and vision tasks, on both training and evaluation data, for Transformers of various configurations, at layers of all depth levels. We discuss how sparsity immediately implies a way to significantly reduce the FLOP count and improve efficiency for Transformers. Moreover, we demonstrate perhaps surprisingly that enforcing an even sparser activation via Top-k thresholding with a small k brings a collection of desired properties, namely less sensitivity to noisy training data, more robustness to input corruptions, and better calibration for their prediction confidence.

NeurIPS Conference 2022 Conference Paper

Decoupled Context Processing for Context Augmented Language Modeling

• Zonglin Li
• Ruiqi Guo
• Sanjiv Kumar

Language models can be augmented with context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have to memorize the massive amount of world knowledge within its internal parameters, leading to better parameter efficiency, interpretability and modularity. In this paper we examined a simple yet effective architecture for incorporating external context into language models based on decoupled $\texttt{Encoder-Decoder}$ architecture. We showed that such a simple architecture achieves competitive results on auto-regressive language modeling and open domain question answering tasks. We also analyzed the behavior of the proposed model which performs grounded context transfer. Finally we discussed the computational implications of such retrieval augmented models.