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We present Stable Video Materials 3D (SViM3D), a framework to predict multi-view consistent physically based rendering (PBR) materials, given a single image. Recently, video diffusion models have been successfully used to reconstruct 3D…

Graphics · Computer Science 2025-11-04 Andreas Engelhardt , Mark Boss , Vikram Voleti , Chun-Han Yao , Hendrik P. A. Lensch , Varun Jampani

We present MeshLLM, a novel framework that leverages large language models (LLMs) to understand and generate text-serialized 3D meshes. Our approach addresses key limitations in existing methods, including the limited dataset scale when…

We introduce Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on both real and synthetic images, such…

Computer Vision and Pattern Recognition · Computer Science 2025-05-01 Nikolaos Sarafianos , Tuur Stuyck , Xiaoyu Xiang , Yilei Li , Jovan Popovic , Rakesh Ranjan

Deep generative models have shown success in generating 3D shapes with different representations. In this work, we propose Neural Volumetric Mesh Generator(NVMG) which can generate novel and high-quality volumetric meshes. Unlike the…

Computer Vision and Pattern Recognition · Computer Science 2022-10-10 Yan Zheng , Lemeng Wu , Xingchao Liu , Zhen Chen , Qiang Liu , Qixing Huang

We present Gen3R, a method that bridges the strong priors of foundational reconstruction models and video diffusion models for scene-level 3D generation. We repurpose the VGGT reconstruction model to produce geometric latents by training an…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Jiaxin Huang , Yuanbo Yang , Bangbang Yang , Lin Ma , Yuewen Ma , Yiyi Liao

This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts, including single images, multi-view images, and text descriptions. The framework consists of 3D shape generation…

Open-world 3D reconstruction models have recently garnered significant attention. However, without sufficient 3D inductive bias, existing methods typically entail expensive training costs and struggle to extract high-quality 3D meshes. In…

Computer Vision and Pattern Recognition · Computer Science 2024-08-20 Minghua Liu , Chong Zeng , Xinyue Wei , Ruoxi Shi , Linghao Chen , Chao Xu , Mengqi Zhang , Zhaoning Wang , Xiaoshuai Zhang , Isabella Liu , Hongzhi Wu , Hao Su

In the realm of vision models, the primary mode of representation is using pixels to rasterize the visual world. Yet this is not always the best or unique way to represent visual content, especially for designers and artists who depict the…

Computer Vision and Pattern Recognition · Computer Science 2024-08-30 Bocheng Zou , Mu Cai , Jianrui Zhang , Yong Jae Lee

Inspired by generative paradigms in image and video, 3D shape generation has made notable progress, enabling the rapid synthesis of high-fidelity 3D assets from a single image. However, current methods still face challenges, including the…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yangguang Li , Xianglong He , Zi-Xin Zou , Zexiang Liu , Wanli Ouyang , Ding Liang , Yan-Pei Cao

Text-to-3D generation has shown great promise in generating novel 3D content based on given text prompts. However, existing generative methods mostly focus on geometric or visual plausibility while ignoring precise physics perception for…

Computer Vision and Pattern Recognition · Computer Science 2024-03-20 Qingshan Xu , Jiao Liu , Melvin Wong , Caishun Chen , Yew-Soon Ong

Driving video generation has achieved much progress in controllability, video resolution, and length, but fails to support fine-grained object-level controllability for diverse driving videos, while preserving the spatiotemporal…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Li-Heng Chen , Ke Cheng , Yahui Liu , Lei Shi , Shi-Sheng Huang , Hongbo Fu

In recent years, 3D Gaussian splatting has emerged as a powerful technique for 3D reconstruction and generation, known for its fast and high-quality rendering capabilities. To address these shortcomings, this paper introduces a novel…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Xianglong He , Junyi Chen , Sida Peng , Di Huang , Yangguang Li , Xiaoshui Huang , Chun Yuan , Wanli Ouyang , Tong He

The ability to generate highly realistic 2D images from mere text prompts has recently made huge progress in terms of speed and quality, thanks to the advent of image diffusion models. Naturally, the question arises if this can be also…

Computer Vision and Pattern Recognition · Computer Science 2023-04-26 Christina Tsalicoglou , Fabian Manhardt , Alessio Tonioni , Michael Niemeyer , Federico Tombari

Artistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to address different aspects of generating 3D content, such as…

Graphics · Computer Science 2023-09-15 Sean Memery , Osmar Cedron , Kartic Subr

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

Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward scalable and reliable simulation for robotics. In this work, we introduce RecGen, a…

Most existing 3D referring expression segmentation (3DRES) methods rely on dense, high-quality point clouds, while real-world agents such as robots and mobile phones operate with only a few sparse RGB views and strict latency constraints.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Changli Wu , Haodong Wang , Jiayi Ji , Yutian Yao , Chunsai Du , Jihua Kang , Yanwei Fu , Liujuan Cao

Existing multi-view 3D object reconstruction methods heavily rely on sufficient overlap between input views, where occlusions and sparse coverage in practice frequently yield severe reconstruction incompleteness. Recent advancements in…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Jiahao Chang , Chongjie Ye , Yushuang Wu , Yuantao Chen , Yidan Zhang , Zhongjin Luo , Chenghong Li , Yihao Zhi , Xiaoguang Han

We propose an analysis-by-synthesis method for fast multi-view 3D reconstruction of opaque objects with arbitrary materials and illumination. State-of-the-art methods use both neural surface representations and neural rendering. While…

Computer Vision and Pattern Recognition · Computer Science 2022-12-09 Markus Worchel , Rodrigo Diaz , Weiwen Hu , Oliver Schreer , Ingo Feldmann , Peter Eisert

Graphics design is important for various applications, including movie production and game design. To create a high-quality scene, designers usually need to spend hours in software like Blender, in which they might need to interleave and…

Computer Vision and Pattern Recognition · Computer Science 2024-08-06 Ian Huang , Guandao Yang , Leonidas Guibas