There are several approaches to generating 3D content, including:
- One-2-3-45++ [17] and One-2-3-45 [25], which use diffusion processes to generate 3D models from 2D images. These methods employ reference attention techniques and CLIP image embeddings as a global condition to fine-tune the Stable Diffusion2 model on the Objaverse dataset.
- NeRF (Neural Radiance Fields) [1], which uses a neural network to represent 3D scenes in a way that allows for efficient rendering. NeRF uses a structure of Gaussian Splatting [2] to represent the 3D scene.
- Story2Motion [11], which generates motion from extensive text solely using a Transformer-based Large Language Model (LLM) and a human motion database. The paper demonstrates how a character model can walk, eat, or dance according to linguistic descriptions.
Challenges in Generating 3D Content
- Lack of Precise Metrics: Currently, there is no standardized metric to evaluate the quality of AI-generated 3D content. Most papers rely on blind user tests for comparison, which can be time-consuming and expensive.
- Difficulty in Generating Realistic Motions: Creating realistic motions that match the input text or image is a challenging task. Technologies for synthesizing human motions through textual descriptions are rapidly advancing, but there is still room for improvement.
Future of AI-Generated 3D Content
- Advancements in Neural Radiance Fields (NeRF): NeRF has shown promising results in generating realistic 3D scenes. Future research may focus on improving the efficiency and accuracy of NeRF models.
- Increased Use of Language Models: Large language models like Transformers have proven effective in generating motions from text. As these models continue to improve, they may play a more significant role in AI-generated 3D content.
Conclusion
Generating 3D content using AI has made significant progress in recent years. From refining existing 3D models to creating them from scratch, researchers have developed various methodologies. However, there are still challenges to overcome, such as the lack of precise metrics and difficulty in generating realistic motions. As technology continues to advance, we can expect improvements in NeRF models and increased use of language models for AI-generated 3D content.
