ContextAnyone: Context-Aware Diffusion for Character-Consistent Text-to-Video Generation

arXiv:2512.07328 · cs.CV, cs.AI · Submitted 2025-12-08 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-12-08

Updated: 2026-09-01

Code: https://github.com/ziyang1106/ContextAnyone

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Consistency — 26× in this paper · explained in 3 episodes
• Diffusion Model — 15× in this paper · explained in 8 episodes
• Diffusion Models — 15× in this paper · explained in 6 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 5× in this paper
• Embeddings — 4× in this paper · explained in 2 episodes
• Temporal consistency — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Personalization — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Diffusion Process — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 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
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation
• OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
• VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
• CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance
• MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement
• SkyReels-A2: Compose Anything in Video Diffusion Transformers
• LTX-Video: Realtime Video Latent Diffusion
• CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
• ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning
• VACE: All-in-One Video Creation and Editing
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Phantom: Subject-consistent video generation via cross-modal alignment
• Decoupled Weight Decay Regularization
• OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation
• Make-A-Video: Text-to-Video Generation without Text-Video Data
• Wan: Open and Advanced Large-Scale Video Generative Models
• ModelScope Text-to-Video Technical Report
• EchoShot: Multi-Shot Portrait Video Generation
• Qwen3 Technical Report
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

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