From Feed-Forward to Flow: Unifying Reconstruction and Generation Is Easier Than You Think

arXiv:2609.32761 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Transformer — 12× in this paper · explained in 2 episodes
• Flow Matching — 10× in this paper · explained in 8 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Point Clouds — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• SceneTok: A Compressed, Diffusable Token Space for 3D Scenes
• ReconViaGen: Towards Accurate Multi-view 3D Object Reconstruction via Generation
• Diffusion Posterior Sampling for General Noisy Inverse Problems
• Generative Modeling via Drifting
• UpFusion: Novel View Diffusion from Unposed Sparse View Observations
• UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis
• Back to Basics: Let Denoising Generative Models Denoise
• Flow Matching for Generative Modeling
• One-step Latent-free Image Generation with Pixel Mean Flows
• DreamFusion: Text-to-3D using 2D Diffusion
• Denoising Diffusion Implicit Models
• Score-Based Generative Modeling through Stochastic Differential Equations
• The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images with Minimal 3D Knowledge
• Novel View Synthesis with Diffusion Models
• RnG: A Unified Transformer for Complete 3D Modeling from Partial Observations
• PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation
• No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images
• PixelDiT: Pixel Diffusion Transformers for Image Generation

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