Collaborative Multi-Modal Coding for High-Quality 3D Generation
Researchers have introduced TriMM, a novel feed-forward generative model designed for high-quality 3D asset creation. TriMM uniquely integrates features from multiple modalities, such as RGB images, RGBD data, and point clouds, to enhance both texture and geometric detail in generated 3D assets. The model employs collaborative multi-modal coding to preserve the distinct strengths of each data type and utilizes auxiliary 2D and 3D supervision to improve robustness. Experiments show that TriMM can achieve competitive performance with significantly less training data compared to existing models. AI
IMPACT This research could lead to more efficient and higher-quality 3D content generation by leveraging diverse data types.