  We present a significant breakthrough in 3D shape generation by scaling it to
unprecedented dimensions. Through the adaptation of the Auto-Regressive model
and the utilization of large language models, we have developed a remarkable
model with an astounding 3.6 billion trainable parameters, establishing it as
the largest 3D shape generation model to date, named Argus-3D. Our approach
addresses the limitations of existing methods by enhancing the quality and
diversity of generated 3D shapes. To tackle the challenges of high-resolution
3D shape generation, our model incorporates tri-plane features as latent
representations, effectively reducing computational complexity. Additionally,
we introduce a discrete codebook for efficient quantization of these
representations. Leveraging the power of transformers, we enable multi-modal
conditional generation, facilitating the production of diverse and visually
impressive 3D shapes. To train our expansive model, we leverage an ensemble of
publicly-available 3D datasets, consisting of a comprehensive collection of
approximately 900,000 objects from renowned repositories such as ModelNet40,
ShapeNet, Pix3D, 3D-Future, and Objaverse. This diverse dataset empowers our
model to learn from a wide range of object variations, bolstering its ability
to generate high-quality and diverse 3D shapes. Extensive experimentation
demonstrate the remarkable efficacy of our approach in significantly improving
the visual quality of generated 3D shapes. By pushing the boundaries of 3D
generation, introducing novel methods for latent representation learning, and
harnessing the power of transformers for multi-modal conditional generation,
our contributions pave the way for substantial advancements in the field. Our
work unlocks new possibilities for applications in gaming, virtual reality,
product design, and other domains that demand high-quality and diverse 3D
objects.
