Camera Control for Text-to-Image Generation via Learning Viewpoint Tokens

arXiv:2604.19954 · cs.CV · Submitted 2026-04-21 · Read on arXiv

cs.CV

Submitted: 2026-04-21

Updated: 2026-04-21

Code: https://github.com/xinsir6/ControlNetPlus

Project page: https://randdl.github.io/viewtoken_control

Terminology

• Accuracy — 15× in this paper · explained in 1 episode
• Fidelity — 12× in this paper · explained in 2 episodes
• Generalization — 12× in this paper · explained in 13 episodes
• Diffusion Model — 10× in this paper · explained in 8 episodes
• Diffusion Models — 9× in this paper · explained in 6 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Embeddings — 8× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Angular Error — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Ab Initio — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes

Sources

• Deep Learning using Rectified Linear Units (ReLU)
• Emerging Properties in Unified Multimodal Pretraining
• SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
• Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
• T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• DreamFusion: Text-to-3D using 2D Diffusion
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
• MVDream: Multi-view Diffusion for 3D Generation
• MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion
• OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation
• Harmonizing Visual Representations for Unified Multimodal Understanding and Generation
• Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
• Reconstruction Alignment Improves Unified Multimodal Models
• Qwen2 Technical Report
• TexVerse: A Universe of 3D Objects with High-Resolution Textures
• Stable Virtual Camera: Generative View Synthesis with Diffusion Models

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