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Recent advances in the diffusion models have significantly improved text-to-image generation. However, generating videos from text is a more challenging task than generating images from text, due to the much larger dataset and higher…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Taegyeong Lee , Soyeong Kwon , Taehwan Kim

Object manipulation in images aims to not only edit the object's presentation but also gift objects with motion. Previous methods encountered challenges in concurrently handling static editing and dynamic generation, while also struggling…

Computer Vision and Pattern Recognition · Computer Science 2025-01-23 Ruisi Zhao , Zechuan Zhang , Zongxin Yang , Yi Yang

The creation of manufacturable and editable 3D shapes through Computer-Aided Design (CAD) remains a highly manual and time-consuming task, hampered by the complex topology of boundary representations of 3D solids and unintuitive design…

Computer Vision and Pattern Recognition · Computer Science 2025-04-10 Md Ferdous Alam , Faez Ahmed

Diffusion models have demonstrated remarkable performance in generating unimodal data across various tasks, including image, video, and text generation. On the contrary, the joint generation of multimodal data through diffusion models is…

Machine Learning · Computer Science 2025-06-16 Kevin Rojas , Yuchen Zhu , Sichen Zhu , Felix X. -F. Ye , Molei Tao

Existing video generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability. To overcome these limitations, we introduce PhysCtrl, a novel framework for…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Chen Wang , Chuhao Chen , Yiming Huang , Zhiyang Dou , Yuan Liu , Jiatao Gu , Lingjie Liu

Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of controllable high-quality image generation from arbitrary text prompts. However, the generation quality and generalization ability of 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-04-23 Ying-Tian Liu , Yuan-Chen Guo , Guan Luo , Heyi Sun , Wei Yin , Song-Hai Zhang

In this paper, we propose VideoFrom3D, a novel framework for synthesizing high-quality 3D scene videos from coarse geometry, a camera trajectory, and a reference image. Our approach streamlines the 3D graphic design workflow, enabling…

Graphics · Computer Science 2025-09-23 Geonung Kim , Janghyeok Han , Sunghyun Cho

We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in under a minute. It supports…

We present a new method for multimodal conditional 3D face geometry generation that allows user-friendly control over the output identity and expression via a number of different conditioning signals. Within a single model, we demonstrate…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Christopher Otto , Prashanth Chandran , Sebastian Weiss , Markus Gross , Gaspard Zoss , Derek Bradley

Recent advances in video generation have enabled the synthesis of high-quality and visually realistic clips using diffusion transformer models. However, most existing approaches operate purely in the 2D pixel space and lack explicit…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Yunpeng Bai , Shaoheng Fang , Chaohui Yu , Fan Wang , Qixing Huang

In this paper, we introduce MeshGen, an advanced image-to-3D pipeline that generates high-quality 3D meshes with detailed geometry and physically based rendering (PBR) textures. Addressing the challenges faced by existing 3D native…

Graphics · Computer Science 2025-05-09 Zilong Chen , Yikai Wang , Wenqiang Sun , Feng Wang , Yiwen Chen , Huaping Liu

We introduce LaviGen, a framework that repurposes 3D generative models for 3D layout generation. Unlike previous methods that infer object layouts from textual descriptions, LaviGen operates directly in the native 3D space, formulating…

Computer Vision and Pattern Recognition · Computer Science 2026-04-20 Haoran Feng , Yifan Niu , Zehuan Huang , Yang-Tian Sun , Chunchao Guo , Yuxin Peng , Lu Sheng

Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap can be observed between these two fields, e.g., scalable 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Sibo Wu , Congrong Xu , Binbin Huang , Andreas Geiger , Anpei Chen

Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. Although diffusion models have achieved notable success in graph generation recently, they are…

Machine Learning · Computer Science 2024-06-04 Jaehyeong Jo , Dongki Kim , Sung Ju Hwang

Recent advances in text-to-video generation have harnessed the power of diffusion models to create visually compelling content conditioned on text prompts. However, they usually encounter high computational costs and often struggle to…

Computer Vision and Pattern Recognition · Computer Science 2024-04-24 Jiaxi Lv , Yi Huang , Mingfu Yan , Jiancheng Huang , Jianzhuang Liu , Yifan Liu , Yafei Wen , Xiaoxin Chen , Shifeng Chen

Recent progress in driving video generation has shown significant potential for enhancing self-driving systems by providing scalable and controllable training data. Although pretrained state-of-the-art generation models, guided by 2D layout…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Yishen Ji , Ziyue Zhu , Zhenxin Zhu , Kaixin Xiong , Ming Lu , Zhiqi Li , Lijun Zhou , Haiyang Sun , Bing Wang , Tong Lu

We present a significant breakthrough in 3D shape generation by scaling it to unprecedented dimensions. Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Yu Wang , Xuelin Qian , Jingyang Huo , Tiejun Huang , Bo Zhao , Yanwei Fu

We introduce PhysMotion, a novel framework that leverages principled physics-based simulations to guide intermediate 3D representations generated from a single image and input conditions (e.g., applied force and torque), producing…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Xiyang Tan , Ying Jiang , Xuan Li , Zeshun Zong , Tianyi Xie , Yin Yang , Chenfanfu Jiang

Generative models have emerged as an essential building block for many image synthesis and editing tasks. Recent advances in this field have also enabled high-quality 3D or video content to be generated that exhibits either multi-view or…

Computer Vision and Pattern Recognition · Computer Science 2023-08-10 Sherwin Bahmani , Jeong Joon Park , Despoina Paschalidou , Hao Tang , Gordon Wetzstein , Leonidas Guibas , Luc Van Gool , Radu Timofte

We present PhysGen, a novel image-to-video generation method that converts a single image and an input condition (e.g., force and torque applied to an object in the image) to produce a realistic, physically plausible, and temporally…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 Shaowei Liu , Zhongzheng Ren , Saurabh Gupta , Shenlong Wang
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