WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation

arXiv:2609.36937 · cs.CV, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Fidelity — 24× in this paper · explained in 2 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 10× in this paper
• Diffusion Model — 10× in this paper · explained in 8 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Identity Binding Supervision — 4× in this paper
• Reference Asymmetric RoPE Conditioning — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 3× in this paper
• Generative AI — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Identity Assignment Accuracy — 1× in this paper
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• CoMPAS3D: A Dataset and Benchmark for Interactive Motion
• MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion
• Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
• Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
• LTX-Video: Realtime Video Latent Diffusion
• CameraCtrl: Enabling Camera Control for Text-to-Video Generation
• ID-Animator: Zero-Shot Identity-Preserving Human Video Generation
• Auto-Encoding Variational Bayes
• Towards Accurate Generative Models of Video: A New Metric & Challenges
• Wan: Open and Advanced Large-Scale Video Generative Models
• Wan-Animate-2: Pushing the Application Boundaries of Character Animation
• SCAIL-2: Unifying Controlled Character Animation with End-to-End In-Context Conditioning
• SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation
• MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance
• CamI2V: Camera-Controlled Image-to-Video Diffusion Model

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