MATE4D: Matrix-Guided Editable 4D Generation from a Single Image

arXiv:2610.11181 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Gaussian Splatting — 14× in this paper · explained in 4 episodes
• Fidelity — 13× in this paper · explained in 2 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 10× in this paper
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Background Editing Module — 6× in this paper
• Ablation — 5× in this paper · explained in 1 episode
• Image Matrix Module — 5× in this paper
• 3D Gaussian Splatting — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 3× in this paper
• Distillation — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• European Conference on Computer Vision — 2× in this paper
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Dynamic Info Input — 1× in this paper
• Dynamic Modeling — 1× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• MATE4D with Stable Video Diffusion — 1× in this paper
• Perceptual Alignment — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Animate124: Animating One Image to 4D Dynamic Scene
• Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
• SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
• LRM: Large Reconstruction Model for Single Image to 3D
• DreamGaussian4D: Generative 4D Gaussian Splatting
• SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency
• Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
• Towards Accurate Generative Models of Video: A New Metric & Challenges
• EG4D: Explicit Generation of 4D Object without Score Distillation
• Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video
• 4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models