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Related papers: PhysX-Anything: Simulation-Ready Physical 3D Asset…

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Simulation-ready physical 3D assets have emerged as a promising direction owing to their broad applicability in downstream tasks. However, most existing 3D generation methods either neglect physical properties or are limited to a single…

Computer Vision and Pattern Recognition · Computer Science 2026-05-22 Ziang Cao , Yinghao Liu , Haitian Li , Runmao Yao , Fangzhou Hong , Zhaoxi Chen , Liang Pan , Ziwei Liu

3D modeling is moving from virtual to physical. Existing 3D generation primarily emphasizes geometries and textures while neglecting physical-grounded modeling. Consequently, despite the rapid development of 3D generative models, the…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Ziang Cao , Zhaoxi Chen , Liang Pan , Ziwei Liu

Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment…

Computer Vision and Pattern Recognition · Computer Science 2025-07-29 Haoyu Zhao , Hao Wang , Xingyue Zhao , Hao Fei , Hongqiu Wang , Chengjiang Long , Hua Zou

Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation. However, creating these articulated objects requires extensive human effort and expertise, limiting…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Long Le , Jason Xie , William Liang , Hung-Ju Wang , Yue Yang , Yecheng Jason Ma , Kyle Vedder , Arjun Krishna , Dinesh Jayaraman , Eric Eaton

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

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

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

Creating interactive digital environments for gaming, robotics, and simulation relies on articulated 3D objects whose functionality emerges from their part geometry and kinematic structure. However, existing approaches remain fundamentally…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Penghao Wang , Siyuan Xie , Hongyu Yan , Xianghui Yang , Jingwei Huang , Chunchao Guo , Jiayuan Gu

Despite advances in physics-based 3D motion synthesis, current methods face key limitations: reliance on pre-reconstructed 3D Gaussian Splatting (3DGS) built from dense multi-view images with time-consuming per-scene optimization; physics…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Chunji Lv , Zequn Chen , Donglin Di , Weinan Zhang , Hao Li , Wei Chen , Yinjie Lei , Changsheng Li

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

We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Xin Huang , Tengfei Wang , Ziwei Liu , Qing Wang

Existing single-image 3D indoor scene generators often produce results that look visually plausible but fail to obey real-world physics, limiting their reliability in robotics, embodied AI, and design. To examine this gap, we introduce a…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Dongli Wu , Jingyu Hu , Ka-Hei Hui , Xiaobao Wei , Chengwen Luo , Jianqiang Li , Zhengzhe Liu

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.…

Recently, significant advancements have been made in the reconstruction and generation of 3D assets, including static cases and those with physical interactions. To recover the physical properties of 3D assets, existing methods typically…

Computer Vision and Pattern Recognition · Computer Science 2025-02-03 Yuchen Lin , Chenguo Lin , Jianjin Xu , Yadong Mu

Foundation models have achieved remarkable success across video, image, and language domains. By scaling up the number of parameters and training datasets, these models acquire generalizable world knowledge and often surpass task-specific…

Machine Learning · Computer Science 2025-07-16 Tung Nguyen , Arsh Koneru , Shufan Li , Aditya Grover

High-quality articulated 3D assets are indispensable for embodied AI and physical simulation, yet 3D generation still focuses on static meshes, leaving a gap in "sim-ready" interactive objects. Most recent articulated object creation…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Chuanrui Zhang , Minghan Qin , Yuang Wang , Baifeng Xie , Hang Li , Ziwei Wang

There are two prevalent ways to constructing 3D scenes: procedural generation and 2D lifting. Among them, panorama-based 2D lifting has emerged as a promising technique, leveraging powerful 2D generative priors to produce immersive,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-31 Yukun Huang , Jiwen Yu , Yanning Zhou , Jianan Wang , Xintao Wang , Pengfei Wan , Xihui 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

We present PhyCAGE, the first approach for physically plausible compositional 3D asset generation from a single image. Given an input image, we first generate consistent multi-view images for components of the assets. These images are then…

Computer Vision and Pattern Recognition · Computer Science 2024-11-28 Han Yan , Mingrui Zhang , Yang Li , Chao Ma , Pan Ji

Existing generative models for 3D shapes can synthesize high-fidelity and visually plausible shapes. For certain classes of shapes that have undergone an engineering design process, the realism of the shape is tightly coupled with the…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Yingxuan You , Chen Zhao , Hantao Zhang , Ming Xu , Pascal Fua
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