WarpHammer: Densifying Scene Warps with 3D Object Priors for Extreme View Synthesis

arXiv:2606.31258 · cs.CV · Submitted 2026-06-30 · Read on arXiv

cs.CV

Submitted: 2026-06-30

Updated: 2026-09-26

Terminology

• Benchmark — 17× in this paper · explained in 2 episodes
• Diffusion Model — 17× in this paper · explained in 8 episodes
• Consistency — 14× in this paper · explained in 3 episodes
• Diffusion Models — 13× in this paper · explained in 6 episodes
• Fidelity — 10× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Accuracy — 5× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Generative Model — 5× in this paper · explained in 2 episodes
• Training-Free Framework — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes

Sources

• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• MVGenMaster: Scaling Multi-View Generation from Any Image via 3D Priors Enhanced Diffusion Model
• ShapeNet: An Information-Rich 3D Model Repository
• VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
• FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis
• SAM 3D: 3Dfy Anything in Images
• CAT3D: Create Anything in 3D with Multi-View Diffusion Models
• CameraCtrl: Enabling Camera Control for Text-to-Video Generation
• EX-4D: EXtreme Viewpoint 4D Video Synthesis via Depth Watertight Mesh
• RayZer: A Self-supervised Large View Synthesis Model
• LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias
• OrbitNVS: Harnessing Video Diffusion Priors for Novel View Synthesis
• Depth Anything 3: Recovering the Visual Space from Any Views
• 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors
• CamCloneMaster: Enabling Reference-based Camera Control for Video Generation
• You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale
• DINOv2: Learning Robust Visual Features without Supervision
• SAM 2: Segment Anything in Images and Videos
• Taming Video Models for 3D and 4D Generation via Zero-Shot Camera Control
• CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models

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