6 papers
Dream, Lift, Animate: From Single Images to Animatable Gaussian Avatars
Marcel C. Bühler, Ye Yuan, Xueting Li +3
We introduce Dream, Lift, Animate (DLA), a novel framework that reconstructs animatable 3D human avatars from a single image. This is achieved by leveraging multi-view generation,…
AdaHuman: Animatable Detailed 3D Human Generation with Compositional Multiview Diffusion
Yangyi Huang, Ye Yuan, Xueting Li +2
Existing methods for image-to-3D avatar generation struggle to produce highly detailed, animation-ready avatars suitable for real-world applications. We introduce AdaHuman, a novel…
GeoMan: Temporally Consistent Human Geometry Estimation using Image-to-Video Diffusion
Gwanghyun Kim, Xueting Li, Ye Yuan +5
Estimating accurate and temporally consistent 3D human geometry from videos is a challenging problem in computer vision. Existing methods, primarily optimized for single images, of…
GENMO: A GENeralist Model for Human MOtion
Jiefeng Li, Jinkun Cao, Haotian Zhang +4
Human motion modeling traditionally separates motion generation and estimation into distinct tasks with specialized models. Motion generation models focus on creating diverse, real…
SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing
Xueting Li, Ye Yuan, Shalini De Mello +5
We introduce SimAvatar, a framework designed to generate simulation-ready clothed 3D human avatars from a text prompt. Current text-driven human avatar generation methods either mo…
BLADE: Single-view Body Mesh Learning through Accurate Depth Estimation
Shengze Wang, Jiefeng Li, Tianye Li +5
Single-image human mesh recovery is a challenging task due to the ill-posed nature of simultaneous body shape, pose, and camera estimation. Existing estimators work well on images…