The field of 3D scene generation and reconstruction is rapidly advancing, with a focus on improving the quality, consistency, and controllability of generated scenes. Recent developments have seen a shift towards more sophisticated methods that can handle complex scenes, dynamic objects, and diverse inputs. Notably, the integration of different modalities, such as images, videos, and text, has become a key area of research, enabling more flexible and powerful scene generation and reconstruction models.
One of the primary challenges in this field is achieving consistent and coherent results, particularly when dealing with dynamic scenes or multiple viewpoints. To address this, researchers have proposed various solutions, including the use of trajectory fields, sparse-to-dense anchored encoding, and dual correspondences. These approaches have shown significant promise in improving the quality and consistency of generated scenes.
Another important aspect of this research is the development of more efficient and scalable methods. This includes the use of diffusion-based models, which have been shown to be highly effective in generating high-quality scenes while reducing computational requirements.
Some noteworthy papers in this area include: Color3D, which presents a framework for controllable and consistent 3D colorization with personalized colorizer. VIST3A, which introduces a general framework for text-to-3D generation by combining the power of a modern latent text-to-video model with the geometric abilities of a recent 3D reconstruction system. FlashWorld, which proposes a generative model that produces 3D scenes from a single image or text prompt in seconds, significantly faster than previous works. MVCustom, which achieves multi-view customization with geometric consistency, addressing the gap between multi-view generation and customization models. Trace Anything, which represents any video as a Trajectory Field, enabling effective spatio-temporal representation and prediction of dynamics in videos. STANCE, which addresses the challenges of maintaining coherent object motion and interactions in video generation through sparse-to-dense anchored encoding. 3D Scene Prompting, which generates the next video chunk from arbitrary-length input while enabling precise camera control and preserving scene consistency. C4D, which recovers 4D scenes from monocular video by leveraging temporal correspondences to extend existing 3D reconstruction formulation to 4D.
