CineAGI is a multi-agent LLM framework that generates multi-scene movies with improved character consistency, narrative coherence, and audio-visual alignment.
High-resolution image synthesis with latent diffu- sion models
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PhysLayer is a framework that decomposes images into depth layers, simulates physics with depth awareness, and synthesizes videos guided by language for more plausible animations.
Contrast-X benchmark and FlowMI model enable synthesis of contrast-enhanced images from arbitrary non-contrast modality inputs using multi-modal flow matching.
FreeGraftor performs subject-driven text-to-image generation without training by cross-image feature grafting via semantic matching, position-constrained attention fusion, and a noise initialization strategy that preserves reference geometry.
citing papers explorer
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CineAGI: Character-Consistent Movie Creation through LLM-Orchestrated Multi-Modal Generation and Cross-Scene Integration
CineAGI is a multi-agent LLM framework that generates multi-scene movies with improved character consistency, narrative coherence, and audio-visual alignment.
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PhysLayer: Language-Guided Layered Animation with Depth-Aware Physics
PhysLayer is a framework that decomposes images into depth layers, simulates physics with depth awareness, and synthesizes videos guided by language for more plausible animations.
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Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching
Contrast-X benchmark and FlowMI model enable synthesis of contrast-enhanced images from arbitrary non-contrast modality inputs using multi-modal flow matching.
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FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation
FreeGraftor performs subject-driven text-to-image generation without training by cross-image feature grafting via semantic matching, position-constrained attention fusion, and a noise initialization strategy that preserves reference geometry.