DreamWaltz: Make a Scene with Complex 3D Animatable AvatarsWe present DreamWaltz, a novel framework for generating and animating complex 3D avatars given text guidance and parametric human body prior. While recent methods have shown encouraging results for text-to-3D generation of common objects, creating high-quaidea-research.github.io DreamWaltz: Make a Scene with Complex 3D Animatable Avatars..

POPDG: Popular 3D Dance Generation with PopDanceSetGenerating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet, the first dataset tailored to the preferences of young audiences, enabling the generationarxiv.org POPDG: Popular 3D Dance Generation with PopDanceSetZhenye Luo, Min Ren, Xuecai Hu,..

Music-driven group choreography는 다양한 어려움이 있지만, 산업 분야에 응용할 분야가 상당히 많기 때문에 최근 들어 연구가 활발하게 진행되고 있는 분야라고 한다.하지만, 대부분의 최근 연구들은 high-fidelity long-term motion을 만들지 못하거나 controllable한 경험을 허용하지 않는다. 그래서 이 논문에서는 high-quality, customizable한 group dance generation을 consistency와 diversity를 효율적으로 제어함으로써 해결하고자 했다고 한다.구체적으로 diffusion-based 방식을 사용해서 input music에 coherent하면서 flexible한 dancer 수와 long-term group d..

EDGE: Editable Dance Generation from MusicEDGE: Editable Dance Generation from Music, 2022.edge-dance.github.io EDGE: Editable Dance Generation From MusicDance is an important human art form, but creating new dances can be difficult and time-consuming. In this work, we introduce Editable Dance GEneration (EDGE), a state-of-the-art method for editable dance generation that is capable of creating ..

3D Gaussian Splatting for Real-Time Radiance Field Rendering[Müller 2022] Müller, T., Evans, A., Schied, C. and Keller, A., 2022. Instant neural graphics primitives with a multiresolution hash encoding [Hedman 2018] Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G. and Brostow, G., 2018. Deep blendingrepo-sam.inria.frAbstractRadiance Field method : 여러 장의 이미지나 비디오로 novel-view synthesi..

Abstractimage-based 3D human shape estimation은 Deep Neural Network의 등장으로 급격히 발전했다. 하지만, real world setting에서는 input image의 detail을 살리는데 어려움을 겪는데, 이 논문의 저자들은 이러한 어려움의 원인을 2가지 conflicting requirements에서 찾았다.Accurate predictions require large context, but precise predictions require high resolutionDue to memory limitations in current hardware, previous approaches tend to take low resolution images ..