MEt3R: Measuring Multi-View Consistency in Generated Images
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Cited by 30 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We introduce MEt3R, a metric for multi-view consistency in generated images. Large-scale generative models for multi-view image generation are rapidly advancing the field of 3D inference from sparse observations. However, due to the nature of generative modeling, traditional reconstruction metrics are not suitable to measure the quality of generated outputs and metrics that are independent of the sampling procedure are desperately needed. In this work, we specifically address the aspect of consistency between generated multi-view images, which can be evaluated independently of the specific scene. Our approach uses DUSt3R to obtain dense 3D reconstructions from image pairs in a feed-forward manner, which are used to warp image contents from one view into the other. Then, feature maps of these images are compared to obtain a similarity score that is invariant to view-dependent effects. Using MEt3R, we evaluate the consistency of a large set of previous methods for novel view and video generation, including our open, multi-view latent diffusion model.
Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). Builds on 46 of its 53 references that are in the map. First posted on arXiv 10 Jan 2025.
Builds on
46 of 53 references.
References · 46
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
- 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
- 2016Improved Techniques for Training GANscited by 2,938
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
- 2022Video Diffusion Modelscited by 2,280
- 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,574
34 more
- 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,526
- 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
- 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,483
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
- 2018Demystifying MMD GANscited by 1,268
- 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,204
- 2024Grounding Image Matching in 3D with MASt3Rcited by 960
- 2023MVDream: Multi-view Diffusion for 3D Generationcited by 928
- 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 781
- 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Imagecited by 638
- 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 601
- 2024CAT3D: Create Anything in 3D with Multi-View Diffusion Modelscited by 425
- 2024SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusioncited by 364
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- 2022SparseFusion: Distilling View-conditioned Diffusion for 3D Reconstructioncited by 216
- 2022DiffRF: Rendering-Guided 3D Radiance Field Diffusioncited by 204
- 2023Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstructioncited by 180
- 2024SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistencycited by 143
- 2022Open-Vocabulary Universal Image Segmentation with MaskCLIPcited by 131
- 2024FeatUp: A Model-Agnostic Framework for Features at Any Resolutioncited by 114
- 2024latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstructioncited by 114
- 2023Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervisioncited by 112
- 2021Geometry-Free View Synthesis: Transformers and no 3D Priorscited by 99
- 2023Long-Term Photometric Consistent Novel View Synthesis with Diffusion Modelscited by 76
- 2023EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusioncited by 72
- 2024GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warpingcited by 72
- 2024MultiDiff: Consistent Novel View Synthesis from a Single Imagecited by 67
- 2024VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Modelscited by 56
- 2024VideoMV: Consistent Multi-View Generation Based on Large Video Generative Modelcited by 45
- 2023Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generationcited by 11
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Citing papers per year: 2025: 37; 2026: 54.
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