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We propose L3DG, the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation. This enables effective generative 3D modeling, scaling to generation of entire room-scale scenes which can be…

Computer Vision and Pattern Recognition · Computer Science 2024-10-18 Barbara Roessle , Norman Müller , Lorenzo Porzi , Samuel Rota Bulò , Peter Kontschieder , Angela Dai , Matthias Nießner

3D reconstruction and simulation, although interrelated, have distinct objectives: reconstruction requires a flexible 3D representation that can adapt to diverse scenes, while simulation needs a structured representation to model motion…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Shaojie Ma , Yawei Luo , Wei Yang , Yi Yang

Large Language Models(LLMs) have revolutionized text generation and multimodal perception,but their capabilities in 3D content generation remain underexplored. Existing methods compromise by producing either low-resolution meshes or coarse…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Junming Huang , Chi Wang , Letian Li , Guangkai Xu , Donglin Huang , Hao Chen , Qiang Dai , Weiwei Xu

We introduce Gaussian Garments, a novel approach for reconstructing realistic simulation-ready garment assets from multi-view videos. Our method represents garments with a combination of a 3D mesh and a Gaussian texture that encodes both…

Computer Vision and Pattern Recognition · Computer Science 2024-09-13 Boxiang Rong , Artur Grigorev , Wenbo Wang , Michael J. Black , Bernhard Thomaszewski , Christina Tsalicoglou , Otmar Hilliges

Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures. These assets typically consist of a single, fused representation, like an implicit neural field, a Gaussian mixture, or a mesh,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Minghao Chen , Roman Shapovalov , Iro Laina , Tom Monnier , Jianyuan Wang , David Novotny , Andrea Vedaldi

3D Gaussian Splatting (3DGS) has demonstrated impressive capabilities in novel view synthesis. However, rendering reflective objects remains a significant challenge, particularly in inverse rendering and relighting. We introduce RTR-GS, a…

Graphics · Computer Science 2025-12-17 Yongyang Zhou , Fang-Lue Zhang , Zichen Wang , Lei Zhang

Large-scale diffusion models have achieved remarkable success in generating high-quality images from textual descriptions, gaining popularity across various applications. However, the generation of layered content, such as transparent…

Computer Vision and Pattern Recognition · Computer Science 2024-12-06 Yusuf Dalva , Yijun Li , Qing Liu , Nanxuan Zhao , Jianming Zhang , Zhe Lin , Pinar Yanardag

Feedforward 3D Gaussian Splatting (3DGS) overcomes the limitations of optimization-based 3DGS by enabling fast and high-quality reconstruction without the need for per-scene optimization. However, existing feedforward approaches typically…

Computer Vision and Pattern Recognition · Computer Science 2025-08-06 Anran Wu , Long Peng , Xin Di , Xueyuan Dai , Chen Wu , Yang Wang , Xueyang Fu , Yang Cao , Zheng-Jun Zha

Recently, 3D Gaussian Splatting (3DGS) has demonstrated excellent ability in small-scale 3D surface reconstruction. However, extending 3DGS to large-scale scenes remains a significant challenge. To address this gap, we propose a novel…

Computer Vision and Pattern Recognition · Computer Science 2024-09-24 YuanZheng Wu , Jin Liu , Shunping Ji

The labor- and experience-intensive creation of 3D assets with physically based rendering (PBR) materials demands an autonomous 3D asset creation pipeline. However, most existing 3D generation methods focus on geometry modeling, either…

Computer Vision and Pattern Recognition · Computer Science 2026-04-07 Ze-Xin Yin , Jiaxiong Qiu , Liu Liu , Xinjie Wang , Wei Sui , Zhizhong Su , Jian Yang , Jin Xie

We introduce GeoGS3D, a novel two-stage framework for reconstructing detailed 3D objects from single-view images. Inspired by the success of pre-trained 2D diffusion models, our method incorporates an orthogonal plane decomposition…

Computer Vision and Pattern Recognition · Computer Science 2024-11-01 Qijun Feng , Zhen Xing , Zuxuan Wu , Yu-Gang Jiang

We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second. Key to our success is a novel dataset of multiview videos…

Computer Vision and Pattern Recognition · Computer Science 2024-06-18 Jiawei Ren , Kevin Xie , Ashkan Mirzaei , Hanxue Liang , Xiaohui Zeng , Karsten Kreis , Ziwei Liu , Antonio Torralba , Sanja Fidler , Seung Wook Kim , Huan Ling

Generating and inserting new objects into 3D content is a compelling approach for achieving versatile scene recreation. Existing methods, which rely on SDS optimization or single-view inpainting, often struggle to produce high-quality…

Computer Vision and Pattern Recognition · Computer Science 2025-04-14 Hongliang Zhong , Can Wang , Jingbo Zhang , Jing Liao

Universal image restoration is a critical task in low-level vision, requiring the model to remove various degradations from low-quality images to produce clean images with rich detail. The challenges lie in sampling the distribution of…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 JiaKui Hu , Zhengjian Yao , Lujia Jin , Yanye Lu

Gaussian splatting has emerged as a powerful tool for high-fidelity reconstruction of dynamic scenes. However, existing methods primarily rely on implicit motion representations, such as encoding motions into neural networks or per-Gaussian…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Xinyu Zhang , Haonan Chang , Yuhan Liu , Abdeslam Boularias

Recently, 3D Gaussian Splatting has emerged as a prominent research direction owing to its ultrarapid training speed and high-fidelity rendering capabilities. However, the unstructured and irregular nature of Gaussian point clouds poses…

Computer Vision and Pattern Recognition · Computer Science 2026-02-16 Xiao Ren , Yu Liu , Ning An , Jian Cheng , Xin Qiao , He Kong

While physically-based rendering (PBR) simulates light transport that guarantees physical realism, achieving true photorealistic rendering (PRR) demands prohibitive time and labor, and still struggles to capture the intractable richness of…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Jiayuan Lu , Rengan Xie , Xuancheng Jin , Zhizhen Wu , Qi Ye , Tian Xie , Hujun Bao , Rui Wang. Yuchi Huo

Recent advancements in neural rendering techniques have significantly enhanced the fidelity of 3D reconstruction. Notably, the emergence of 3D Gaussian Splatting (3DGS) has marked a significant milestone by adopting a discrete scene…

Computer Vision and Pattern Recognition · Computer Science 2024-05-24 Licheng Shen , Ho Ngai Chow , Lingyun Wang , Tong Zhang , Mengqiu Wang , Yuxing Han

3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization.However, the original 3D Gaussian Splatting lacks color representation for inputs in…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Haoran Wang , Jingwei Huang , Lu Yang , Tianchen Deng , Gaojing Zhang , Mingrui Li

Graphics pipelines require physically-based rendering (PBR) materials, yet current 3D content generation approaches are built on RGB models. We propose to model the PBR image distribution directly, avoiding photometric inaccuracies in RGB…

Computer Vision and Pattern Recognition · Computer Science 2024-08-26 Shimon Vainer , Mark Boss , Mathias Parger , Konstantin Kutsy , Dante De Nigris , Ciara Rowles , Nicolas Perony , Simon Donné