Autoregressive 3D Scene Generation

Latest papers

14 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Sparse auto-regressive modeling for scene generation from multi-view images
Sep 3, 2026Thomas Lucas, Maxime Pietrantoni, Philippe Weinzaepfel +4Sparse-View 3D Reconstruction 3D Scene Generation
1NAVER LABS Europe, France · 2NAVER LABS, South Korea · 3Carnegie Mellon University, USA
OctWorld: Long-Range World-Consistent Video Generation with Octree-Based 3D Mapping
Sep 3, 2026Zelong Lv, Sicheng Xu, Jianfeng Xiang +53D Scene Representation Camera-Controlled Video Generation
University of Science and Technology of China · Microsoft Research Asia · Tsinghua University
MegaParts: Scaling Part-Aware 3D Object Generation to 300 Parts via Token-Efficient Autoregressive Modeling
Aug 14, 2026Manwen Liao, Xinyu Lian, Jian Mao +11Autoregressive 3D Scene Generation Part-Level 3D Generation
The University of Hong Kong · Shanghai Artificial Intelligence Laboratory · Fudan University +5
SubdivAR: Autoregressive Next-Scale Prediction for Neural Mesh Subdivision
Jun 25, 2026Huipeng Guo, Zikai Song, Hang Long +73D Mesh Generation Neural Representation Geometry
Huazhong University of Science and Technology
SuperVoxelGPT: Adaptive and Ordered 3D Tokenization for Autoregressive Shape Generation
May 28, 2026Yuan Li, Congyi Zhang, Xifeng Gao +13D Shape Generation Autoregressive Generation
University of Texas at Dallas, USA · LightSpeed, USA
Rollback-Free Stable Brick Structures Generation
May 7, 2026Chenhui Xu, Ziyue Bai, Fuxun Yu +23D Shape Generation Autoregressive Generation
University of Buffalo · University of Maryland, College Park · Microsoft +1
Repurposing 3D Generative Model for Autoregressive Layout Generation
Apr 17, 2026Haoran Feng, Yifan Niu, Zehuan Huang +43D Scene Generation Autoregressive Generation
School of Software, Beihang University · 2Tsinghua University · University of Hong Kong +2
Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion
Apr 17, 2026Zhenggang Tang, Yuehao Wang, Yuchen Fan +9Autoregressive Diffusion 3D Scene Generation
Meta Reality Labs · University of Illinois Urbana-Champaign · University of Texas at Austin
Omni123: Exploring 3D Native Foundation Models with Limited 3D Data by Unifying Text to 2D and 3D Generation
Apr 2, 2026Chongjie Ye, Cheng Cao, Chuanyu Pan +4Autoregressive 3D Scene Generation 3D Foundation Models
FNii-Shenzhen · SSE, CUHK(SZ) · Meshy AI
ARGS: Auto-Regressive Gaussian Splatting via Parallel Progressive Next-Scale Prediction
Apr 1, 2026Quanyuan Ruan, Kewei Shi, Jiabao Lei +2Autoregressive 3D Scene Generation 3D Asset Generation
South China University of Technology · The University of Hong Kong · The Chinese University of Hong Kong, Shenzhen +1
GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation
Mar 27, 2026Nicolas von Lützow, Barbara Rössle, Katharina Schmid +13D Gaussian Splatting 3D Scene Generation
Technical University of Munich, Germany
LoST: Level of Semantics Tokenization for 3D Shapes
Mar 18, 2026Niladri Shekhar Dutt, Zifan Shi, Paul Guerrero +4Autoregressive 3D Scene Generation 3D Shape Generation
University College London · Adobe Research
PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction
Mar 6, 2026Xiang Zhang, Sohyun Yoo, Hongrui Wu +3Autoregressive 3D Scene Generation 3D Mesh Reconstruction
UC San Diego · Lambda, Inc.
LaGen: Towards Autoregressive LiDAR Scene Generation
Nov 26, 2025Sizhuo Zhou, Xiaosong Jia, Fanrui Zhang +73D Scene Generation Autoregressive Generation
University of Science and Technology of China, Hefei, Anhui 230026, China · Shanghai Innovation Institute, Shanghai 200231, China · Fudan University, Shanghai 200433, China +1

Sep 3, 2026J/K move · Enter open · S save

Sparse auto-regressive modeling for scene generation from multi-view images

Thomas Lucas, Maxime Pietrantoni, Philippe Weinzaepfel, Wonjune Cho+3

1NAVER LABS Europe, France · 2NAVER LABS, South Korea · 3Carnegie Mellon University, USA

Sparse-View 3D Reconstruction 3D Scene Generation 3D Scene Representation Autoregressive Generation 3D Scene Completion Autoregressive 3D Scene Generation

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Generating complete 3D scenes from sparse, unconstrained views is a fundamental challenge in 3D vision which requires reasoning beyond observed content while remaining computationally tractable. Existing feed-forward reconstruction methods are inherently limited to content visible in the input images, while 3D generative modeling is hindered by the high computational cost of dense volumetric representations and the scarcity of large-scale 3D supervision. We introduce SPAR3S, a sparse voxel-aligned 3D latent generative model for conditional scene completion without requiring ground-truth 3D data for supervision. Our key insight is to formulate 3D scene generation in a structured, compact, voxel-aligned 3D latent space where only occupied voxels are represented. We learn this sparse latent space directly from multi-view images using photometric supervision via differentiable 3D Gaussian Splatting. Given a partial set of observed voxels encoded from sparse input views, scene completion reduces to predicting the missing latent tokens and their spatial support within the voxel grid. To this end, we train a masked autoregressive transformer that jointly models voxel occupancy and latent token values, enabling efficient and spatially consistent generation of unseen regions. We demonstrate the effectiveness of our method on synthetic indoor scenes, achieving higher novel-view quality than prior work. We further validate its generalization on RealEstate10k, highlighting its applicability to real-world data.