vector-quantized

MegaParts introduces a scalable framework for part-aware 3D object generation using token-efficient vector-quantized tokens and autoregressive modeling, enabling generation of objects with up to 300 parts.

GEAR proposes a method to jointly train a vector-quantized tokenizer and autoregressive generator end-to-end via representation alignment, achieving up to 10x faster convergence on ImageNet gFID compared to strong baselines.