The recent advancements in novel view synthesis (NVS) and 3D reconstruction have seen a shift towards more integrated and flexible frameworks that leverage multi-level geometry learning and generative models. A notable trend is the incorporation of skeleton-guided approaches and multi-view synthesis techniques, which enhance both the accuracy and consistency of generated views. These methods often employ diffusion models and neural fields to improve the quality of 3D reconstructions, particularly in scenarios with sparse or unposed images. The integration of geometric priors and the elimination of explicit alignment processes are key innovations that are making NVS more accessible and robust. Notably, the use of skeleton guidance and multi-level geometry learning is proving to be particularly effective in improving the fidelity of 3D human reconstructions from monocular images. These developments are paving the way for more sophisticated and adaptable NVS systems that can handle a variety of input conditions and produce high-quality outputs.

Noteworthy Papers:

• CRAYM: Introduces camera ray matching for joint optimization of camera poses and neural fields, significantly enhancing both geometry reconstruction and photorealistic rendering.
• NVComposer: Proposes a novel approach that eliminates the need for explicit external alignment, improving model accessibility and synthesis quality with increasing input views.