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Topology-preserving 3D generation now achieves 98% fewer tokens and 58.5% faster inference by encoding shapes as overlapping slice latents instead of expensive voxels.
GraphVid achieves superior video quality and controllability with significantly less training data, revolutionizing how we can interact with multi-object video generation.
Achieve SOTA multimodal performance across eight benchmarks and strong zero-shot generalization without task-specific training by decoupling understanding and generation via unified discrete flow matching.
