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Related papers: Helix4D: Complex 4D Mesh Generation

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Meshes serve as a primary representation for 3D assets. Autoregressive mesh generators serialize faces into sequences and train on truncated segments with sliding-window inference to cope with memory limits. However, this mismatch breaks…

Computer Vision and Pattern Recognition · Computer Science 2025-12-10 Junkai Lin , Hang Long , Huipeng Guo , Jielei Zhang , JiaYi Yang , Tianle Guo , Yang Yang , Jianwen Li , Wenxiao Zhang , Matthias Nießner , Wei Yang

While recent 3D generative models can produce high-quality texture images, they often fail to capture human preferences or meet task-specific requirements. Moreover, a core challenge in the 3D texture generation domain is that most existing…

Computer Vision and Pattern Recognition · Computer Science 2025-12-10 AmirHossein Zamani , Tianhao Xie , Amir G. Aghdam , Tiberiu Popa , Eugene Belilovsky

With the rapid advancement and widespread adoption of VR/AR technologies, there is a growing demand for the creation of high-quality, immersive dynamic scenes. However, existing generation works predominantly concentrate on the creation of…

Computer Vision and Pattern Recognition · Computer Science 2025-08-22 Ke Xing , Hanwen Liang , Dejia Xu , Yuyang Yin , Konstantinos N. Plataniotis , Yao Zhao , Yunchao Wei

Human Mesh Recovery (HMR) aims to reconstruct 3D human pose and shape from 2D observations and is fundamental to human-centric understanding in real-world scenarios. While recent image-based HMR methods such as SAM 3D Body achieve strong…

Computer Vision and Pattern Recognition · Computer Science 2025-12-10 Mingqi Gao , Yunqi Miao , Jungong Han

Given a monocular video, the goal of video re-rendering is to generate views of the scene from a novel camera trajectory. Existing methods face two distinct challenges. Geometrically unconditioned models lack spatial awareness, leading to…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Mingyang Xie , Numair Khan , Tianfu Wang , Naina Dhingra , Seonghyeon Nam , Haitao Yang , Zhuo Hui , Christopher Metzler , Andrea Vedaldi , Hamed Pirsiavash , Lei Luo

Recently, interactive digital human video generation has attracted widespread attention and achieved remarkable progress. However, building such a practical system that can interact with diverse input signals in real time remains…

Computer Vision and Pattern Recognition · Computer Science 2025-08-29 Ming Chen , Liyuan Cui , Wenyuan Zhang , Haoxian Zhang , Yan Zhou , Xiaohan Li , Songlin Tang , Jiwen Liu , Borui Liao , Hejia Chen , Xiaoqiang Liu , Pengfei Wan

Remarkable advances have been achieved recently in learning neural representations that characterize object geometry, while generating textured objects suitable for downstream applications and 3D rendering remains at an early stage. In…

Computer Vision and Pattern Recognition · Computer Science 2023-04-13 Alexey Bokhovkin , Shubham Tulsiani , Angela Dai

In this paper, we present Consistent4D, a novel approach for generating 4D dynamic objects from uncalibrated monocular videos. Uniquely, we cast the 360-degree dynamic object reconstruction as a 4D generation problem, eliminating the need…

Computer Vision and Pattern Recognition · Computer Science 2023-11-07 Yanqin Jiang , Li Zhang , Jin Gao , Weimin Hu , Yao Yao

World models serve as essential building blocks toward Artificial General Intelligence (AGI), enabling intelligent agents to predict future states and plan actions by simulating complex physical interactions. However, existing interactive…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Junyi Chen , Haoyi Zhu , Xianglong He , Yifan Wang , Jianjun Zhou , Wenzheng Chang , Yang Zhou , Zizun Li , Zhoujie Fu , Jiangmiao Pang , Tong He

Triangle meshes are fundamental to 3D applications, enabling efficient modification and rasterization while maintaining compatibility with standard rendering pipelines. However, current automatic mesh generation methods typically rely on…

Computer Vision and Pattern Recognition · Computer Science 2025-08-15 Yuxuan Wang , Xuanyu Yi , Haohan Weng , Qingshan Xu , Xiaokang Wei , Xianghui Yang , Chunchao Guo , Long Chen , Hanwang Zhang

Advances in generative modeling have significantly enhanced digital content creation, extending from 2D images to complex 3D and 4D scenes. Despite substantial progress, producing high-fidelity and temporally consistent dynamic 4D content…

Computer Vision and Pattern Recognition · Computer Science 2025-08-01 DongFu Yin , Xiaotian Chen , Fei Richard Yu , Xuanchen Li , Xinhao Zhang

Recent advances in 3D scene editing using NeRF and 3DGS enable high-quality static scene editing. In contrast, dynamic scene editing remains challenging, as methods that directly extend 2D diffusion models to 4D often produce motion…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Shifeng Chen , Yihui Li , Jun Liao , Hongyu Yang , Di Huang

Video-guided 3D animation holds immense potential for content creation, offering intuitive and precise control over dynamic assets. However, practical deployment faces a critical yet frequently overlooked hurdle: the pose misalignment…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Zijie Wu , Lixin Xu , Puhua Jiang , Sicong Liu , Chunchao Guo , Xiang Bai

The availability of high-speed 3D video sensors has greatly facilitated 3D shape acquisition of dynamic and deformable objects, but high frame rate 3D reconstruction is always degraded by spatial noise and temporal fluctuations. This paper…

Computer Vision and Pattern Recognition · Computer Science 2017-08-08 Jie Zhang , Christos Maniatis , Luis Horna , Robert B. Fisher

With the increasing demand for 3D animation, generating high-fidelity, controllable 4D avatars from textual descriptions remains a significant challenge. Despite notable efforts in 4D generative modeling, existing methods exhibit…

Computer Vision and Pattern Recognition · Computer Science 2025-11-21 Eddie Pokming Sheung , Qihao Liu , Wufei Ma , Prakhar Kaushik , Jianwen Xie , Alan Yuille

Generating high-quality meshes with complex structures and realistic surfaces is the primary goal of 3D generative models. Existing methods typically employ sequence data or deformable tetrahedral grids for mesh generation. However,…

Computer Vision and Pattern Recognition · Computer Science 2024-10-24 Ruowei Wang , Jiaqi Li , Dan Zeng , Xueqi Ma , Zixiang Xu , Jianwei Zhang , Qijun Zhao

The visual inspection of a hexahedral mesh with respect to element quality is difficult due to clutter and occlusions that are produced when rendering all element faces or their edges simultaneously. Current approaches overcome this problem…

Graphics · Computer Science 2021-04-23 Christoph Neuhauser , Junpeng Wang , Rüdiger Westermann

High-quality textures are critical for realistic 3D content creation, yet existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference image. To address these challenges, we propose a…

Computer Vision and Pattern Recognition · Computer Science 2025-09-08 Arianna Rampini , Kanika Madan , Bruno Roy , AmirHossein Zamani , Derek Cheung

\textbf{Synthetic human dynamics} aims to generate photorealistic videos of human subjects performing expressive, intention-driven motions. However, current approaches face two core challenges: (1) \emph{geometric inconsistency} and…

Computer Vision and Pattern Recognition · Computer Science 2025-08-14 Weiqi Li , Zehao Zhang , Liang Lin , Guangrun Wang

Reconstructing dynamic 4D scenes is challenging, as it requires robust disentanglement of dynamic objects from the static background. While 3D foundation models like VGGT provide accurate 3D geometry, their performance drops markedly when…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yu Hu , Chong Cheng , Sicheng Yu , Xiaoyang Guo , Hao Wang