Although neural rendering has made significant ad-vances in creating lifelike, animatable full-body and head avatars, incorporating detailed expressions into full-body avatars remains largely unexplored. We present DEGAS, the first 3D Gaussian Splatting (3DGS)-based modeling method for full-body avatars with rich facial expressions. Trained on multiview videos of a given subject, our method learns a conditional variational autoencoder that takes both the body motion and facial expression as driving signals to generate Gaussian maps in the UV layout. To drive the facial expressions, instead of the commonly used 3D Mor-phable Models (3DMMs) in 3D head avatars, we propose to adopt the expression latent space trained solely on 2D portrait images, bridging the gap between 2D talking faces and 3D avatars. Leveraging the rendering capability of 3DGS and the rich expressiveness of the expression latent space, the learned avatars can be reenacted to reproduce photo-realistic rendering images with subtle and accurate facial expressions. Experiments on an existing dataset and our newly proposed dataset offull-body talking avatars demonstrate the efficacy of our method. We also propose an audio-driven extension of our method with the help of 2D talking faces, opening new possibilities for interactive AI agents. Project page: https://initialneil.github.io/DEGAS.

Current text-to-avatar methods often rely on implicit representations (e.g., NeRF, SDF, and DMTet), leading to 3D content that artists cannot easily edit and animate in graphics software. This paper introduces a novel framework for generating stylized head avatars from text guidance, which leverages locally learnable mesh deformation and 2D diffusion priors to achieve high-quality digital assets for attribute-preserving manipulation. Given a template mesh, our method represents mesh deformation with perface Jacobians and adaptively modulates local deformation using a learnable vector field. This vector field enables anisotropic scaling while preserving the rotation of vertices, which can better express identity and geometric details. We also employ landmark- and contour-based regularization terms to balance the expressiveness and plausibility of generated head avatars from multiple views without relying on any specific shape prior. Our framework can generate realistic shapes and textures that can be further edited via text, while supporting seamless editing using the preserved attributes from the template mesh, such as 3DMM parameters, blendshapes, and UV coordinates. Extensive experiments demonstrate that our framework can generate diverse and expressive head avatars with high-quality meshes that artists can easily manipulate in 3D graphics software, facilitating downstream applications such as efficient asset creation and animation with preserved attributes.

Professional 3D asset creation often requires diverse sculpting brushes to add surface details and geometric structures. Despite recent progress in 3D generation, producing reusable sculpting brushes compatible with artists' workflows remains an open and challenging problem. These sculpting brushes are typically represented as vector displacement maps (VDMs), which existing models cannot easily generate compared to natural images. This paper presents Text2VDM, a novel framework for text-to-VDM brush generation through the deformation of a dense planar mesh guided by score distillation sampling (SDS). The original SDS loss is designed for generating full objects and struggles with generating desirable sub-object structures from scratch in brush generation. We refer to this issue as semantic coupling, which we address by introducing weighted blending of prompt tokens to SDS, resulting in a more accurate target distribution and semantic guidance. Experiments demonstrate that Text2VDM can generate diverse, high-quality VDM brushes for sculpting surface details and geometric structures. Our generated brushes can be seamlessly integrated into mainstream modeling software, enabling various applications such as mesh stylization and real-time interactive modeling.

Scroll images are a unique medium commonly used in virtual reality (VR) providing an immersive visual storytelling experience. Despite rapid advances in diffusion-based image generation, it remains an open research question to generate scroll images suitable for immersive, coherent, and controllable storytelling in VR. This paper proposes a multi-layered, diffusion-based scroll image generation framework with a novel semantic-aware denoising process. We incorporate layout prediction and style control modules to generate coherent scroll images of any aspect ratio. Based on the scroll image generation framework, we use different multi-window strategies to render diverse visual forms such as chains, rings, and forks for VR storytelling. Quantitative and qualitative evaluations demonstrate that our techniques can significantly enhance text-image consistency and visual coherence in scroll image generation, as well as the level of immersion and engagement of VR storytelling. We will release our source code to facilitate better collaborations on immersive storytelling between AI researchers and creative practitioners. https://magicscroll.github.io/

The creation of 3D content, crucial in various applications, is often challenging and time-intensive. While digital tools are prevalent for 3D content creation, traditional clay sculpting offers an embodied experience that fosters artists’ perceptual engagement with physical space, enhancing their interactive and cognitive connection with the creation process. We conducted an eight-day live sculpting session at an art academy, systematically comparing the creative workflows of eight professional artists in both physical and digital mediums. Our qualitative and quantitative analysis include artists’ differences in tool usage between physical and digital sculpting, variations in visual and tactile perceptual engagement, and the potential for future integration of the two modalities. Our study provides insights into the benefits of physical and digital sculpting and may inform future design of hybrid interfaces for 3D content creation.