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Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention

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arxiv 2405.11616 v3 pith:EYEH5G3F submitted 2024-05-19 cs.CV

Peng Li , Yuan Liu , Xiaoxiao Long , Feihu Zhang , Cheng Lin , Mengfei Li , Xingqun Qi , Shanghang Zhang
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Wenhan Luo Ping Tan Wenping Wang Qifeng Liu Yike Guo
This is my paper
classification cs.CV
keywords multiviewera3dimagesattentioncameradiffusionmethodsefficient
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In this paper, we introduce Era3D, a novel multiview diffusion method that generates high-resolution multiview images from a single-view image. Despite significant advancements in multiview generation, existing methods still suffer from camera prior mismatch, inefficacy, and low resolution, resulting in poor-quality multiview images. Specifically, these methods assume that the input images should comply with a predefined camera type, e.g. a perspective camera with a fixed focal length, leading to distorted shapes when the assumption fails. Moreover, the full-image or dense multiview attention they employ leads to an exponential explosion of computational complexity as image resolution increases, resulting in prohibitively expensive training costs. To bridge the gap between assumption and reality, Era3D first proposes a diffusion-based camera prediction module to estimate the focal length and elevation of the input image, which allows our method to generate images without shape distortions. Furthermore, a simple but efficient attention layer, named row-wise attention, is used to enforce epipolar priors in the multiview diffusion, facilitating efficient cross-view information fusion. Consequently, compared with state-of-the-art methods, Era3D generates high-quality multiview images with up to a 512*512 resolution while reducing computation complexity by 12x times. Comprehensive experiments demonstrate that Era3D can reconstruct high-quality and detailed 3D meshes from diverse single-view input images, significantly outperforming baseline multiview diffusion methods. Project page: https://penghtyx.github.io/Era3D/.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Stream3D: Sequential Multi-View 3D Generation via Evidential Memory

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    Stream3D is a training-free method that maintains temporal consistency in 3D generation from monocular streams by dynamically caching a fixed number of informative historical frames using an evidence score.

  2. MV-RAG: Retrieval Augmented Multiview Diffusion

    cs.CV 2025-08 conditional novelty 7.0 of 10

    A retrieval-augmented multiview diffusion model conditions on web images to generate 3D-consistent views of rare concepts, trained with a hybrid 3D/2D objective and evaluated on a new OOD benchmark.

  3. Stream3D: Sequential Multi-View 3D Generation via Evidential Memory

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    Stream3D is a training-free method that maintains a fixed-size evidential memory of past frames to convert frozen view-conditioned 3D generators into consistent streaming generators.

  4. ROAR-3D: Routing Arbitrary Views for High-Fidelity 3D Generation

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    ROAR-3D adds a token-wise view router and dual-stream attention to pretrained single-view 3D generators so they can use arbitrary unposed images for higher-fidelity output.

  5. Make Your MoVe: Make Your 3D Contents by Adapting Multi-View Diffusion Models to External Editing

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A tuning-free dual-pipeline that injects original normal latents into an edited multi-view diffusion stream, preserving geometry during 2D-to-3D appearance editing.

  6. DualMat: PBR Material Estimation via Coherent Dual-Path Diffusion

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    DualMat is a dual-path diffusion model combining an albedo-optimized pretrained latent path with a material-specialized compact latent path, using feature distillation and rectified flow to estimate PBR materials from...

  7. 4DVD: Cascaded Dense-view Video Diffusion Model for High-quality 4D Content Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A two-stage cascaded video diffusion model generates 16-view consistent videos from a monocular video, enabling higher-quality 4D content reconstruction.

  8. TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models

    cs.CV 2025-02 unverdicted novelty 6.0 of 10

    TripoSG generates high-fidelity 3D meshes from input images via a large-scale rectified flow transformer and hybrid-trained 3D VAE on a custom 2-million-sample dataset, claiming state-of-the-art fidelity and generalization.

  9. Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

    cs.CV 2025-01 unverdicted novelty 4.0 of 10

    Hunyuan3D 2.0 scales flow-based diffusion transformers and texture synthesis models to generate high-resolution textured 3D assets that outperform prior state-of-the-art in geometry, alignment, and texture quality.

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