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ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image

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arxiv 2310.17994 v2 pith:VPHEIWLR submitted 2023-10-27 cs.CV cs.GR

Kyle Sargent , Zizhang Li , Tanmay Shah , Charles Herrmann , Hong-Xing Yu , Yunzhi Zhang , Eric Ryan Chan , Dmitry Lagun
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Li Fei-Fei Deqing Sun Jiajun Wu
This is my paper · ORCID
classification cs.CVcs.GR
keywords novelscenesbackgroundsdataproposesynthesisviewzeronvs
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked backgrounds, we propose new techniques to address challenges introduced by in-the-wild multi-object scenes with complex backgrounds. Specifically, we train a generative prior on a mixture of data sources that capture object-centric, indoor, and outdoor scenes. To address issues from data mixture such as depth-scale ambiguity, we propose a novel camera conditioning parameterization and normalization scheme. Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art result in LPIPS on the DTU dataset in the zero-shot setting, even outperforming methods specifically trained on DTU. We further adapt the challenging Mip-NeRF 360 dataset as a new benchmark for single-image novel view synthesis, and demonstrate strong performance in this setting. Our code and data are at http://kylesargent.github.io/zeronvs/

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Cited by 12 Pith papers

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

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    A single pixel-space diffusion model jointly performs 3D scene reconstruction and generation by supervising flow matching on rendered multi-view images, matching SOTA reconstruction and outperforming latent-space generation.

  3. LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis

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    Initializing a highway encoder-decoder NVS network from VGGT 3D-aware features yields 31.4 PSNR on RealEstate10k with real-time decoding and optional unposed inputs.

  4. Reevaluating the Intra-Modal Misalignment Hypothesis in CLIP

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    Diffusing in a unified 3D representation (geometry plus appearance and semantics) produces more cross-view-consistent 3D Gaussian scenes than 2D latent pipelines.

  5. CharacterShot: Controllable and Consistent 4D Character Animation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A new pipeline generates pose-controlled, view-consistent 4D character animations from one reference image and a 2D pose sequence, backed by a new 13,115-character dataset and benchmark.

  6. Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Vid-CamEdit re-synthesizes monocular videos along user-defined camera paths by conditioning a video diffusion model on 2D flows derived from estimated 3D geometry, without training on multi-view video data.

  7. Towards In-the-wild 3D Plane Reconstruction from a Single Image

    cs.CV 2025-06 conditional novelty 6.0 of 10

    ZeroPlane trains a Transformer plane reconstructor on 560K images spanning 10 indoor and outdoor datasets and outperforms prior methods in zero-shot evaluations on NYUv2, 7-Scenes, ParallelDomain, and ApolloScape.

  8. Pippo: High-Resolution Multi-View Humans from a Single Image

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    A single-image multi-view diffusion transformer generates 1K-resolution turnaround views of humans, with attention biasing for many views and a new reprojection-error metric.

  9. Matrix3D: Large Photogrammetry Model All-in-One

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A single multi-modal diffusion transformer trained with masked learning performs pose estimation, depth prediction, and novel view synthesis in one model, reporting SOTA pose and NVS numbers.

  10. UniAvatar: Taming Lifelike Audio-Driven Talking Head Generation with Comprehensive Motion and Lighting Control

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    UniAvatar integrates FLAME-based 3D motion rendering and SH-based illumination rendering into a diffusion talking-head model, enabling separate or combined control of motion and lighting in generated videos.

  11. Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A feed-forward system that generates object-level and scene-level 3D Gaussian scenes from text in about eight seconds by diffusing multi-view RGB-D latent codes and decoding them into pixel-aligned 3D Gaussians.

  12. Dynamic View Synthesis as an Inverse Problem

    cs.CV 2025-06 reject novelty 3.0 of 10

    Dynamic view synthesis from a monocular video is achieved by redesigning the noise initialization of a pretrained video diffusion model using a recursive interpolation and a stochastic latent modulation.

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