MSR: Multiple Subject Reference for Video Generation

arXiv:2609.18393 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-09-16

Terminology

• Transformer — 12× in this paper · explained in 2 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Multiple Subject Reference — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Personalization — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• ID-LoRA: Identity-Driven Audio-Video Personalization with In-Context LoRA
• MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement
• Ingredients: Blending Custom Photos with Video Diffusion Transformers
• LTX-2: Efficient Joint Audio-Visual Foundation Model
• LoRA: Low-Rank Adaptation of Large Language Models
• BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration
• Flow Matching for Generative Modeling
• RoFormer: Enhanced Transformer with Rotary Position Embedding
• Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment

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