Advances in 3D Generation and View Synthesis

The recent advancements in the field of 3D character generation and novel view synthesis are pushing the boundaries of what is possible in virtual reality, gaming, and filmmaking. Innovations in diffusion models and transformer-based architectures are enabling the creation of high-quality, semantically decomposed 3D assets with unprecedented speed and detail. These models are not only enhancing the realism and customization of 3D characters but also expanding the capabilities of novel view synthesis through protective covers, which is crucial for extended reality applications. Additionally, the integration of audio-driven facial dynamics and head motion generation is advancing the field of character animation, allowing for more natural and expressive interactions. The trend towards more efficient and scalable solutions, such as those leveraging pixel-space diffusion models, is evident, with a focus on reducing runtime and improving the quality of generated content. These developments collectively indicate a shift towards more integrated and versatile systems that can handle complex tasks with greater accuracy and speed.

Sources

StdGEN: Semantic-Decomposed 3D Character Generation from Single Images

Through the Curved Cover: Synthesizing Cover Aberrated Scenes with Refractive Field

Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models

Edify 3D: Scalable High-Quality 3D Asset Generation

Novel View Synthesis with Pixel-Space Diffusion Models

MikuDance: Animating Character Art with Mixed Motion Dynamics

JoyVASA: Portrait and Animal Image Animation with Diffusion-Based Audio-Driven Facial Dynamics and Head Motion Generation

LES-Talker: Fine-Grained Emotion Editing for Talking Head Generation in Linear Emotion Space

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