Author name cluster

Xiaowei Zhou

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

1 author row

AAAI Conference 2026 Conference Paper

StreamingTalker: Audio-driven 3D Facial Animation with Autoregressive Diffusion Model

Yifan Yang
Zhi Cen
Sida Peng
Xiangwei Chen
Yifu Deng
Xinyu Zhu
Fan Jia
Xiaowei Zhou

This paper focuses on the task of speech-driven 3D facial animation, which aims to generate realistic and synchronized facial motions driven by speech inputs. Recent methods have employed audio-conditioned diffusion models for 3D facial animation, achieving impressive results in generating expressive and natural animations. However, these methods process the whole audio sequences in a single pass, which poses two major challenges: they tend to perform poorly when handling audio sequences that exceed the training horizon and will suffer from significant latency when processing long audio inputs. To address these limitations, we propose a novel autoregressive diffusion model that outputs facial motions in a streaming manner. This design ensures flexibility with varying audio lengths and achieves low latency independent of audio duration. Specifically, we select a limited number of past frames as historical motion context and combine them with the audio input to create a dynamic condition. This condition guides a lightweight diffusion head to iteratively generate facial motion frames, enabling real-time synthesis with high-quality results. Experiments conducted on public datasets demonstrate that our approach outperforms recent baseline methods.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos

Zhen Xu
Zhengqin Li
Zhao Dong
Xiaowei Zhou
Richard Newcombe
Zhaoyang Lv

We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic components, enabling the modeling of complex, time-varying environments with varying object lifespans. We proposed a novel density control strategy in training, which enables our 4DGT to handle longer space-time input. Our model processes 64 consecutive posed frames in a rolling-window fashion, predicting consistent 4D Gaussians in the scene. Unlike optimization-based methods, 4DGT performs purely feed-forward inference, reducing reconstruction time from hours to seconds and scaling effectively to long video sequences. Trained only on large-scale monocular posed video datasets, 4DGT can outperform prior Gaussian-based networks significantly in real-world videos and achieve on-par accuracy with optimization-based methods on cross-domain videos.

PDF Details

TMLR Journal 2025 Journal Article

Graph Potential Field Neural Network for Massive Agents Group-wise Path Planning

Yueming LYU
Xiaowei Zhou
Xingrui Yu
Ivor Tsang

Multi-agent path planning is important in both multi-agent path finding and multi-agent reinforcement learning areas. However, continual group-wise multi-agent path planning that requires the agents to perform as a team to pursue high team scores instead of individually is less studied. To address this problem, we propose a novel graph potential field-based neural network (GPFNN), which models a valid potential field map for path planning. Our GPFNN unfolds the T-step iterative optimization of the potential field maps as a T-layer feedforward neural network. Thus, a deeper GPFNN leads to more precise potential field maps without the over-smoothing issue. A potential field map inherently provides a monotonic potential flow from any source node to the target nodes to construct the optimal path (w.r.t. the potential decay), equipping our GPFNN with an elegant planning ability. Moreover, we incorporate dynamically updated boundary conditions into our GPFNN to address group-wise multi-agent path planning that supports both static targets and dynamic moving targets. Empirically, experiments on three different-sized mazes (up to $1025 \times 1025$ sized mazes) with up to 1,000 agents demonstrate the planning ability of our GPFNN to handle both static and dynamic moving targets. Experiments on extensive graph node classification tasks on six graph datasets (up to millions of nodes) demonstrate the learning ability of our GPFNN.

PDF Details

NeurIPS Conference 2024 Conference Paper

RobIR: Robust Inverse Rendering for High-Illumination Scenes

Ziyi Yang
Yanzhen Chen
Xinyu Gao
Yazhen Yuan
Yu Wu
Xiaowei Zhou
Xiaogang Jin

Implicit representation has opened up new possibilities for inverse rendering. However, existing implicit neural inverse rendering methods struggle to handle strongly illuminated scenes with significant shadows and slight reflections. The existence of shadows and reflections can lead to an inaccurate understanding of the scene, making precise factorization difficult. To this end, we present RobIR, an implicit inverse rendering approach that uses ACES tone mapping and regularized visibility estimation to reconstruct accurate BRDF of the object. By accurately modeling the indirect radiance field, normal, visibility, and direct light simultaneously, we are able to accurately decouple environment lighting and the object's PBR materials without imposing strict constraints on the scene. Even in high-illumination scenes with shadows and specular reflections, our method can recover high-quality albedo and roughness with no shadow interference. RobIR outperforms existing methods in both quantitative and qualitative evaluations.

PDF Details DOI

NeurIPS Conference 2023 Conference Paper

Compact Neural Volumetric Video Representations with Dynamic Codebooks

Haoyu Guo
Sida Peng
Yunzhi Yan
Linzhan Mou
Yujun Shen
Hujun Bao
Xiaowei Zhou

This paper addresses the challenge of representing high-fidelity volumetric videos with low storage cost. Some recent feature grid-based methods have shown superior performance of fast learning implicit neural representations from input 2D images. However, such explicit representations easily lead to large model sizes when modeling dynamic scenes. To solve this problem, our key idea is reducing the spatial and temporal redundancy of feature grids, which intrinsically exist due to the self-similarity of scenes. To this end, we propose a novel neural representation, named dynamic codebook, which first merges similar features for the model compression and then compensates for the potential decline in rendering quality by a set of dynamic codes. Experiments on the NHR and DyNeRF datasets demonstrate that the proposed approach achieves state-of-the-art rendering quality, while being able to achieve more storage efficiency. The source code is available at https: //github. com/zju3dv/compact_vv.

PDF Details

IJCAI Conference 2022 Conference Paper

Learning to Estimate Object Poses without Real Image Annotations

Haotong Lin
Sida Peng
Zhize Zhou
Xiaowei Zhou

This paper presents a simple yet effective approach for learning 6DoF object poses without real image annotations. Previous methods have attempted to train pose estimators on synthetic data, but they do not generalize well to real images due to the sim-to-real domain gap and produce inaccurate pose estimates. We find that, in most cases, the synthetically trained pose estimators are able to provide reasonable initialization for depth-based pose refinement methods which yield accurate pose estimates. Motivated by this, we propose a novel learning framework, which utilizes the accurate results of depth-based pose refinement methods to supervise the RGB-based pose estimator. Our method significantly outperforms previous self-supervised methods on several benchmarks. Even compared with fully-supervised methods that use real annotated data, we achieve competitive results without using any real annotation. The code is available at https: //github. com/zju3dv/pvnet-depth-sup.

PDF Details DOI

NeurIPS Conference 2022 Conference Paper

OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models

Xingyi He
Jiaming Sun
Yuang Wang
Di Huang
Hujun Bao
Xiaowei Zhou

We propose a new method for object pose estimation without CAD models. The previous feature-matching-based method OnePose has shown promising results under a one-shot setting which eliminates the need for CAD models or object-specific training. However, OnePose relies on detecting repeatable image keypoints and is thus prone to failure on low-textured objects. We propose a keypoint-free pose estimation pipeline to remove the need for repeatable keypoint detection. Built upon the detector-free feature matching method LoFTR, we devise a new keypoint-free SfM method to reconstruct a semi-dense point-cloud model for the object. Given a query image for object pose estimation, a 2D-3D matching network directly establishes 2D-3D correspondences between the query image and the reconstructed point-cloud model without first detecting keypoints in the image. Experiments show that the proposed pipeline outperforms existing one-shot CAD-model-free methods by a large margin and is comparable to CAD-model-based methods on LINEMOD even for low-textured objects. We also collect a new dataset composed of 80 sequences of 40 low-textured objects to facilitate future research on one-shot object pose estimation. The supplementary material, code and dataset are available on the project page: https: //zju3dv. github. io/onepose plus plus/.

PDF Details

NeurIPS Conference 2022 Conference Paper

TotalSelfScan: Learning Full-body Avatars from Self-Portrait Videos of Faces, Hands, and Bodies

Junting Dong
Qi Fang
Yudong Guo
Sida Peng
Qing Shuai
Xiaowei Zhou
Hujun Bao

Recent advances in implicit neural representations make it possible to reconstruct a human-body model from a monocular self-rotation video. While previous works present impressive results of human body reconstruction, the quality of reconstructed face and hands are relatively low. The main reason is that the image region occupied by these parts is very small compared to the body. To solve this problem, we propose a new approach named TotalSelfScan, which reconstructs the full-body model from several monocular self-rotation videos that focus on the face, hands, and body, respectively. Compared to recording a single video, this setting has almost no additional cost but provides more details of essential parts. To learn the full-body model, instead of encoding the whole body in a single network, we propose a multi-part representation to model separate parts and then fuse the part-specific observations into a single unified human model. Once learned, the full-body model enables rendering photorealistic free-viewpoint videos under novel human poses. Experiments show that TotalSelfScan can significantly improve the reconstruction and rendering quality on the face and hands compared to the existing methods. The code is available at \url{https: //zju3dv. github. io/TotalSelfScan}.

PDF Details

AAAI Conference 2022 Conference Paper

Visual Sound Localization in the Wild by Cross-Modal Interference Erasing

Xian Liu
Rui Qian
Hang Zhou
Di Hu
Weiyao Lin
Ziwei Liu
Bolei Zhou
Xiaowei Zhou

The task of audiovisual sound source localization has been well studied under constrained scenes, where the audio recordings are clean. However, in real world scenarios, audios are usually contaminated by off screen sound and background noise. They will interfere with the procedure of identifying desired sources and building visual sound connections, making previous studies nonapplicable. In this work, we propose the Interference Eraser (IEr) framework, which tackles the problem of audiovisual sound source localization in the wild. The key idea is to eliminate the interference by redefining and carving discriminative audio representations. Specifically, we observe that the previous practice of learning only a single audio representation is insufficient due to the additive nature of audio signals. We thus extend the audio representation with our Audio Instance Identifier module, which clearly distinguishes sounding instances when audio signals of different volumes are unevenly mixed. Then we erase the influence of the audible but off screen sounds and the silent but visible objects by a Cross modal Referrer module with cross modality distillation. Quantitative and qualitative evaluations demonstrate that our framework achieves superior results on sound localization tasks, especially under real world scenarios.

PDF Details

NeurIPS Conference 2019 Conference Paper

GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs

Yuan Liu
Zehong Shen
Zhixuan Lin
Sida Peng
Hujun Bao
Xiaowei Zhou

Finding local correspondences between images with different viewpoints requires local descriptors that are robust against geometric transformations. An approach for transformation invariance is to integrate out the transformations by pooling the features extracted from transformed versions of an image. However, the feature pooling may sacrifice the distinctiveness of the resulting descriptors. In this paper, we introduce a novel visual descriptor named Group Invariant Feature Transform (GIFT), which is both discriminative and robust to geometric transformations. The key idea is that the features extracted from the transformed versions of an image can be viewed as a function defined on the group of the transformations. Instead of feature pooling, we use group convolutions to exploit underlying structures of the extracted features on the group, resulting in descriptors that are both discriminative and provably invariant to the group of transformations. Extensive experiments show that GIFT outperforms state-of-the-art methods on several benchmark datasets and practically improves the performance of relative pose estimation.

PDF Details