Multi-View Diffusion Models

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13 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

GroundControl: Satellite-Conditioned Multi-View Diffusion for Reconstruction-Free Novel View Synthesis
Oct 27, 2026Akshay Krishnan, Matias Turkulainen, Jamie Wynn +7Novel View Synthesis Multi-View Diffusion Models
Niantic Spatial · Georgia Institute of Technology · Aalto University
Generative Structure from Motion with Native 3D Diffusion
Oct 26, 2026Haobin Duan, Binbin Huang, Mochu Xiang +4Structure from Motion Multi-View 3D Reconstruction
The University of Hong Kong · Transengram · Tencent
CompMVR: Compression-Aware Multi-View Restoration Using Diffusion Models for Geometrically Consistent 3D Reconstruction
Oct 23, 2026Dong-hwi Kim, Chaewon Moon, Hojun Song +13Multi-View Consistency Multi-View 3D Reconstruction
Kyungpook National University, Republic of Korea · Adobe Research, USA
Synchronous Multi-view Neural Diffusion
Sep 30, 2026Yongquan Shi, Weijun Huang, Yueyang Pi +3Multi-View Diffusion Models Multi-View Learning
Fujian Normal University Fuzhou, China · Fuzhou University Fuzhou, China
StreetDiff: Multi-view Street Scenes Generation via Cross-view Consistent Multi-view Stable Diffusion with Structure Prompts
Sep 9, 2026Qi Zhang, Yanyifan Wang, Weiyuan Zhang +1Multi-View Consistency Multi-View Diffusion Models
College of Computer Science and Software Engineering, Shenzhen University, China
Reflection-aware Generative Novel View Synthesis
Sep 4, 2026GeonU Kim, Shin Dong-Yeon, Tae-Hyun OhMulti-View Diffusion Models Novel View Synthesis
KAIST
SplatGuide: Geometric Priors from 3D Gaussians for Pose-Free Novel View Synthesis
Aug 17, 2026Yejun Zhang, Zihan Wang, Xu Ji +8Multi-View Diffusion Models Novel View Synthesis
Aalto University, Finland · Deep Render, UK · ELLIS Institute Finland +3
MV-Forcing: Long Multi-View Video Generation via 4D-Grounded Spatio-Temporal Self-Forcing
Jul 6, 2026Gal Fiebelman, Hadar Averbuch-Elor, Sagie BenaimMulti-View Consistency Video Diffusion Models
The Hebrew University of Jerusalem · Cornell University
DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing
May 16, 2026Jinxin Ai, Matthias Nießner, Ziya ErkoçMulti-View Diffusion Models Diffusion Models
Technical University of Munich
Repurposing Geometric Foundation Models for Multi-view Diffusion
Mar 23, 2026Wooseok Jang, Seonghu Jeon, Jisang Han +5Multi-View Diffusion Models Novel View Synthesis
KAIST AI · New York University · Intel Labs
Intrinsic Geometry-Appearance Consistency Optimization for Sparse-View Gaussian Splatting
Mar 3, 2026Kaiqiang Xiong, Rui Peng, Jiahao Wu +5Multi-View Diffusion Models 3D Human Reconstruction
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University · Peng Cheng Laboratory · Alibaba Group +2
Direct Reward Fine-Tuning on Poses for Single Image to 3D Human in the Wild
Mar 3, 2026Seunguk Do, Minwoo Huh, Joonghyuk Shin +1Multi-View Diffusion Models 3D Human Reconstruction
Seoul National University
PSHuman: Photorealistic Single-image 3D Human Reconstruction using Cross-Scale Multiview Diffusion and Explicit Remeshing
Sep 16, 2024Peng Li, Wangguandong Zheng, Yuan Liu +9Multi-View Diffusion Models 3D Reconstruction
HKUST · Southeast University · Tsinghua University +1

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GroundControl: Satellite-Conditioned Multi-View Diffusion for Reconstruction-Free Novel View Synthesis

Akshay Krishnan, Matias Turkulainen, Jamie Wynn, Zawar Qureshi+6

Niantic Spatial · Georgia Institute of Technology · Aalto University

Novel View Synthesis Multi-View Diffusion Models

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Multi-view or video diffusion models have recently emerged as state-of-the-art approaches for novel-view synthesis from sparse image collections. While they can produce high quality images, they tend to hallucinate unrealistic spatial layouts when synthesizing views outside the input views (scene extrapolation). We propose to address this challenge for outdoor scenes by using satellite images as an additional information source to condition the multi-view generative model. We introduce a new satellite-conditioned multi-view diffusion model that combines conditioning from a satellite image and street-level input images when generating novel-views. Our cross-attention based conditioning allows us to retain pretrained multi-view diffusion priors to generate seamless, realistic, novel views from a handful of source views, while also adhering to the scene layout as seen from satellite images. Our model does not use 3D scene reconstruction, and only relies on posed street and satellite views. Our experiments on outdoor datasets show that our model is better than both satellite-conditioned or street-view conditioned baselines on novel-view synthesis, especially on challenging cases where target views have very little overlap with source views. Project page: https://nianticspatial.github.io/groundcontrol