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In recent years, significant advancements have been made in text-driven 3D content generation. However, several challenges remain. In practical applications, users often provide extremely simple text inputs while expecting high-quality 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-15 Huiqi Wu , Jianbo Mei , Yingjie Huang , Yining Xu , Jingjiao You , Yilong Liu , Li Yao

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

We propose conformal generative modeling, a framework for generative modeling on 2D surfaces approximated by discrete triangle meshes. Our approach leverages advances in discrete conformal geometry to develop a map from a source triangle…

Machine Learning · Computer Science 2023-03-21 Victor Dorobantu , Charlotte Borcherds , Yisong Yue

Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise motion sequences, failing to effectively control the temporal…

Computer Vision and Pattern Recognition · Computer Science 2023-09-13 Yin Wang , Zhiying Leng , Frederick W. B. Li , Shun-Cheng Wu , Xiaohui Liang

We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera poses. We introduce a novel framework that generates such…

Computer Vision and Pattern Recognition · Computer Science 2023-05-31 Rafail Fridman , Amit Abecasis , Yoni Kasten , Tali Dekel

The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, there is a growing interest in exploring the application of…

Computer Vision and Pattern Recognition · Computer Science 2023-06-21 Ariel Lapid , Idan Achituve , Lior Bracha , Ethan Fetaya

While video-generation-based embodied world models have gained increasing attention, their reliance on large-scale embodied interaction data remains a key bottleneck. The scarcity, difficulty of collection, and high dimensionality of…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Hao Li , Qiao Sun

Generating speech-driven 3D talking heads presents numerous challenges; among those is dealing with varying mesh topologies where no point-wise correspondence exists across the meshes the model can animate. While previous literature works…

Computer Vision and Pattern Recognition · Computer Science 2026-01-09 Federico Nocentini , Thomas Besnier , Claudio Ferrari , Sylvain Arguillere , Mohamed Daoudi , Stefano Berretti

Recent video foundation models demonstrate impressive visual synthesis but frequently suffer from geometric inconsistencies. While existing methods attempt to inject 3D priors via architectural modifications, they often incur high…

Computer Vision and Pattern Recognition · Computer Science 2026-05-27 Weijie Wang , Xiaoxuan He , Youping Gu , Yifan Yang , Zeyu Zhang , Yefei He , Yanbo Ding , Xirui Hu , Donny Y. Chen , Zhiyuan He , Yuqing Yang , Bohan Zhuang

We present a technique for zero-shot generation of a 3D model using only a target text prompt. Without any 3D supervision our method deforms the control shape of a limit subdivided surface along with its texture map and normal map to obtain…

Computer Vision and Pattern Recognition · Computer Science 2022-09-07 Nasir Mohammad Khalid , Tianhao Xie , Eugene Belilovsky , Tiberiu Popa

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

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

Current text-to-image generation models often struggle to follow textual instructions, especially the ones requiring spatial reasoning. On the other hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable precision in…

Computer Vision and Pattern Recognition · Computer Science 2023-05-31 Tianjun Zhang , Yi Zhang , Vibhav Vineet , Neel Joshi , Xin Wang

Text-to-image generation has made remarkable progress with the emergence of diffusion models. However, it is still a difficult task to generate images for street views based on text, mainly because the road topology of street scenes is…

Computer Vision and Pattern Recognition · Computer Science 2024-02-08 Jinming Su , Songen Gu , Yiting Duan , Xingyue Chen , Junfeng Luo

Previous efforts have managed to generate production-ready 3D assets from text or images. However, these methods primarily employ NeRF or 3D Gaussian representations, which are not adept at producing smooth, high-quality geometries required…

Graphics · Computer Science 2024-10-15 Rengan Xie , Wenting Zheng , Kai Huang , Yizheng Chen , Qi Wang , Qi Ye , Wei Chen , Yuchi Huo

Text-to-Motion (T2M) generation aims to synthesize realistic human motion sequences from natural language descriptions. While two-stage frameworks leveraging discrete motion representations have advanced T2M research, they often neglect…

Computer Vision and Pattern Recognition · Computer Science 2026-03-11 Hongsong Wang , Wenjing Yan , Qiuxia Lai , Xin Geng

In text-to-video (T2V) generation, significant attention has been directed toward its development, yet unifying discrete and continuous grounding conditions in T2V generation remains under-explored. This paper proposes a Grounded…

Computer Vision and Pattern Recognition · Computer Science 2024-07-08 Huanzhang Dou , Ruixiang Li , Wei Su , Xi Li

Automatically generating photorealistic and self-consistent appearances for untextured 3D models is a critical challenge in digital content creation. The advancement of large-scale video generation models offers a natural approach: directly…

Computer Vision and Pattern Recognition · Computer Science 2026-03-19 Yan Zeng , Haoran Jiang , Kaixin Yao , Qixuan Zhang , Longwen Zhang , Lan Xu , Jingyi Yu

World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely image-based forecasting or reasoning over partial 3D geometry, limiting their ability to…

Computer Vision and Pattern Recognition · Computer Science 2026-05-27 Jiaxu Wang , Yicheng Jiang , Tianlun He , Jingkai Sun , Qiang Zhang , Junhao He , Jiahang Cao , Zesen Gan , Mingyuan Sun , Qiming Shao , Xiangyu Yue

Generating high-quality camera-controllable videos from monocular input is a challenging task, particularly under extreme viewpoint. Existing methods often struggle with geometric inconsistencies and occlusion artifacts in boundaries,…

Computer Vision and Pattern Recognition · Computer Science 2025-06-09 Tao Hu , Haoyang Peng , Xiao Liu , Yuewen Ma