Research map / 3D vision / 3D generation
NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes NVComposer, a pose-free multi-view generative novel view synthesis model that jointly generates target views and camera poses of unposed condition images via a dual-stream video diffusion model, plus a geometry-aware feature alignment adapter distilled from DUSt3R.
Problem
Generative novel view synthesis from multiple sparse, unposed images currently depends on external multi-view alignment (explicit pose estimation or pre-reconstruction), which fails when views have small overlap or occlusion.
Approach
An image-pose dual-stream diffusion model built on DynamiCrafter that denoises both target-view images and Plücker-ray pose embeddings of condition views (with a separate pose decoding head), combined with a training-only adapter that aligns internal features to DUSt3R pointmaps.
Key results
- On RealEstate10K with 4 input views, NVComposer reaches 22.55 PSNR on easy subset versus 18.48 for ViewCrafter's best (4-view) in the same table.
- On DL3DV, NVComposer PSNR rises monotonically from 15.31 (1 view) to 17.92 (4 views), while ViewCrafter stays around 15.0 to 15.7.
- Implicit pose estimation on hard RealEstate10K pairs gives rotation error 5.86 vs 58.40 for DUSt3R.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase unposed input views from 1 to 4 | NVComposer · RealEstate10K easy | PSNR: 18.7227 -> 22.5519 | Helped |
| Increase unposed input views from 1 to 4 | NVComposer · RealEstate10K medium | PSNR: 15.3101 -> 19.5346 | Helped |
| Increase unposed input views from 1 to 4 | NVComposer · RealEstate10K hard | PSNR: 15.2115 -> 17.8181 | Helped |
| Increase unposed input views from 1 to 4 | NVComposer · DL3DV | PSNR: 15.3101 -> 17.9248 | Helped |
| Increase unposed input views from 1 to 4 | NVComposer · Objaverse test set | PSNR: 16.3764 -> 17.7234 | Helped |
| Increase input views from 1 to 2 | ViewCrafter · RealEstate10K hard | PSNR: 14.0781 -> 13.5859 | Hurt |
| Add image-pose dual-stream diffusion (ablation) | NVComposer · Objaverse | PSNR: 14.6857 -> 17.0510 | Helped |
| Add geometry-aware feature alignment (ablation) | NVComposer · RealEstate10K | PSNR: 14.7218 -> 15.6568 | Helped |
Surprising findings
- ViewCrafter's performance drops when going from one to two input views on the hard RealEstate10K subset, attributed to unstable external alignment.
- Implicit pose estimation outperforms DUSt3R on hard sparse-view pairs, though it is comparable on easy pairs.
Limitations
- Relies on DUSt3R features during training, requiring a pretrained dense stereo model
- Evaluation on Objaverse and RealEstate10K subsets is small (e.g., 20 and 100 samples), limiting statistical strength
Experimental setup
Datasets: Objaverse RealEstate10K CO3D DL3DV
Benchmarks: RealEstate10K DL3DV Objaverse
Metrics: PSNR SSIM LPIPS DISTS FID FVD KVD rotation angle error translation error
Baselines: MotionCtrl CameraCtrl DUSt3R ViewCrafter SV3D
Builds on: DynamiCrafter DUSt3R CameraCtrl ViewCrafter
Closest papers by meaning
- NAMVIS: Next-Scale Autoregressive Multi-View Image Synthesis (NeurIPS 2026 · similarity 0.90)
- Novel View Synthesis with Pixel-Space Diffusion Models (CVPR 2025 · similarity 0.90)
- MVGenMaster: Scaling Multi-View Generation from Any Image via 3D Priors Enhanced Diffusion Model (CVPR 2025 · similarity 0.89)
- UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis (NeurIPS 2025 · similarity 0.88)
- MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views (NeurIPS 2024 · similarity 0.88)
- LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias (ICLR 2025 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
