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Existing image-to-video generation methods often produce physically implausible motions and lack precise control over object dynamics. While prior approaches have incorporated physics simulators, they remain confined to 2D planar motions…

Computer Vision and Pattern Recognition · Computer Science 2026-04-28 Tianyidan Xie , Zhentao Huang , Mingjie Wang , Xin Huang , Jun Zhou , Minglun Gong , Zili Yi

Video generation models nowadays are capable of generating visually realistic videos, but often fail to adhere to physical laws, limiting their ability to generate physically plausible videos and serve as ''world models''. To address this…

Computer Vision and Pattern Recognition · Computer Science 2025-10-16 Sihui Ji , Xi Chen , Xin Tao , Pengfei Wan , Hengshuang Zhao

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

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

Interactive world models that simulate object dynamics are crucial for robotics, VR, and AR. However, it remains a significant challenge to learn physics-consistent dynamics models from limited real-world video data, especially for…

Computer Vision and Pattern Recognition · Computer Science 2025-10-27 Yu Yang , Zhilu Zhang , Xiang Zhang , Yihan Zeng , Hui Li , Wangmeng Zuo

Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and…

Computer Vision and Pattern Recognition · Computer Science 2024-10-23 Bohan Zeng , Shanglin Li , Yutang Feng , Ling Yang , Hong Li , Sicheng Gao , Jiaming Liu , Conghui He , Wentao Zhang , Jianzhuang Liu , Baochang Zhang , Shuicheng Yan

Envisioning physically plausible outcomes from a single image requires a deep understanding of the world's dynamics. To address this, we introduce PhysGen3D, a novel framework that transforms a single image into an amodal, camera-centric,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Boyuan Chen , Hanxiao Jiang , Shaowei Liu , Saurabh Gupta , Yunzhu Li , Hao Zhao , Shenlong Wang

Transforming static images into interactive experiences remains a challenging task in computer vision. Tackling this challenge holds the potential to elevate mobile user experiences, notably through interactive and AR/VR applications.…

Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlooking the functional properties essential for interaction. We…

Computer Vision and Pattern Recognition · Computer Science 2026-05-07 Yunhan Yang , Chunshi Wang , Junliang Ye , Yang Li , Zanxin Chen , Zehuan Huang , Yao Mu , Zhuo Chen , Chunchao Guo , Xihui Liu

In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and customizing their behaviors. However, current 3D generative…

Computer Vision and Pattern Recognition · Computer Science 2024-06-12 Fangfu Liu , Hanyang Wang , Shunyu Yao , Shengjun Zhang , Jie Zhou , Yueqi Duan

Recent advances in 3D content generation have amplified demand for dynamic models that are both visually realistic and physically consistent. However, state-of-the-art video diffusion models frequently produce implausible results such as…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Siwei Meng , Yawei Luo , Ping Liu

4D content generation aims to create dynamically evolving 3D content that responds to specific input objects such as images or 3D representations. Current approaches typically incorporate physical priors to animate 3D representations, but…

Computer Vision and Pattern Recognition · Computer Science 2025-11-04 Jiajing Lin , Zhenzhong Wang , Dejun Xu , Shu Jiang , YunPeng Gong , Min Jiang

While recent video generation models have achieved significant visual fidelity, they often suffer from the lack of explicit physical controllability and plausibility. To address this, some recent studies attempted to guide the video…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Haoze Zhang , Tianyu Huang , Zichen Wan , Xiaowei Jin , Hongzhi Zhang , Hui Li , Wangmeng Zuo

Video Diffusion Models (VDMs) offer a promising approach for simulating dynamic scenes and environments, with broad applications in robotics and media generation. However, existing models often generate temporally incoherent content that…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Zhexiao Xiong , Yizhi Song , Liu He , Wei Xiong , Yu Yuan , Feng Qiao , Nathan Jacobs

We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. While recent advancements have addressed specific facets of human-object interactions, a comprehensive physics-based approach…

Robotics · Computer Science 2023-09-15 Jona Braun , Sammy Christen , Muhammed Kocabas , Emre Aksan , Otmar Hilliges

Interactable objects are ubiquitous in our daily lives. Recent advances in 3D generative models make it possible to automate the modeling of these objects, benefiting a range of applications from 3D printing to the creation of robot…

Computer Vision and Pattern Recognition · Computer Science 2025-02-05 Rundong Luo , Haoran Geng , Congyue Deng , Puhao Li , Zan Wang , Baoxiong Jia , Leonidas Guibas , Siyuan Huang

Generative models for 3D object synthesis have seen significant advancements with the incorporation of prior knowledge distilled from 2D diffusion models. Nevertheless, challenges persist in the form of multi-view geometric inconsistencies…

Computer Vision and Pattern Recognition · Computer Science 2023-11-20 Lincong Feng , Muyu Wang , Maoyu Wang , Kuo Xu , Xiaoli Liu

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

Recent advances in video generation have shown promise for generating future scenarios, critical for planning and control in autonomous driving and embodied intelligence. However, real-world applications demand more than visually plausible…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Tianshuo Xu , Zhifei Chen , Leyi Wu , Hao Lu , Yuying Chen , Lihui Jiang , Bingbing Liu , Yingcong Chen

Recently, the surge of efficient and automated 3D AI-generated content (AIGC) methods has increasingly illuminated the path of transforming human imagination into complex 3D structures. However, the automated generation of 3D content is…

Graphics · Computer Science 2024-12-20 Pei Chen , Fudong Wang , Yixuan Tong , Jingdong Chen , Ming Yang , Minghui Yang
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