Vitality-Aware Compression for Efficient Image-to-Shape Diffusion Transformers

We propose the first compression approach for image-to-shape Diffusion Transformers (DiTs) that substantially reduces modelsize while preserving geometric fidelity. Despite remarkable progress in3D shape generation, large DiT-based models remain computationallyprohibitive in resource-constrained settings. Furthermore, it is difficult todirectly transfer existing diffusion model compression strategies developedfor different domains to 3D generation, and prior 3D efficiency approachesfocus primarily on inference speed rather than backbone compression. Toaddress this limitation, we build a geometry-aware compression frameworktailored to image-to-shape DiTs. Guided by the observation that 3DDiT layers exhibit non-uniform importance for geometry synthesis, weintroduce a vitality-guided framework integrating structured pruning,adaptive quantization, and targeted fine-tuning. Our method achieves upto 66% model-size reduction across state-of-the-art image-to-3D modelswhile maintaining synthesis fidelity comparable to full-sized counterparts.This highlights the potential of our framework as a plug-and-play solutionfor efficient 3D shape generation across diverse models.