Democratizing the Creation of Animatable Facial Avatars
In high-end visual effects pipelines, a customized (and expensive) light stage system is (typically) used to scan an actor in order to acquire both geometry and texture for various expressions. Aiming towards democratization, we propose a novel pipeline for obtaining geometry and texture as well as enough expression information to build a customized person-specific animation rig without using a light stage or any other high-end hardware (or manual cleanup). A key novel idea consists of warping real-world images to align with the geometry of a template avatar and subsequently projecting the warped image into the template avatar's texture; importantly, this allows us to leverage baked-in real-world lighting/texture information in order to create surrogate facial features (and bridge the domain gap) for the sake of geometry reconstruction. Not only can our method be used to obtain a neutral expression geometry and de-lit texture, but it can also be used to improve avatars after they have been imported into an animation system (noting that such imports tend to be lossy, while also hallucinating various features). Since a default animation rig will contain template expressions that do not correctly correspond to those of a particular individual, we use a Simon Says approach to capture various expressions and build a person-specific animation rig (that moves like they do). Our aforementioned warping/projection method has high enough efficacy to reconstruct geometry corresponding to each expressions.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text
We study the problem of creating high-fidelity and animatable 3D avatars from only textual descriptions. Existing text-to-avatar methods are either limited to static avatars which cannot be animated or struggle to genera…
NeRFPERSE: Personalized 3D Generative Avatars from A Single Portrait
We present PERSE, a method for building an animatable personalized generative avatar from a reference portrait. Our avatar model enables facial attribute editing in a continuous and disentangled latent space to control e…
AttributeHeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting
Creating digital avatars from textual prompts has long been a desirable yet challenging task. Despite the promising results achieved with 2D diffusion priors, current methods struggle to create high-quality and consisten…
Bridging the Gap: Studio-like Avatar Creation from a Monocular Phone Capture
Creating photorealistic avatars for individuals traditionally involves extensive capture sessions with complex and expensive studio devices like the LightStage system. While recent strides in neural representations have …
3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting
We introduce an approach that creates animatable human avatars from monocular videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields (NeRFs) achieve high-quality novel-view/novel-pose…
3DGSImage Generation