2 citations · 2 across the 10 of their papers we have counts for
11 papers
GRAIL: Generating Humanoid Loco-Manipulation from World Models
Tianyi Xie, Haotian Zhang, Jinhyung Park +17
Scaling humanoid loco-manipulation requires robot-compatible demonstrations across diverse objects, whole-body motions, and scene geometries, but teleoperation and motion capture a…
Kimodo: Scaling Controllable Human Motion Generation
Davis Rempe, Mathis Petrovich, Ye Yuan +21
High-quality human motion data is becoming increasingly important for applications in robotics, simulation, and entertainment. Recent generative models offer a potential data sourc…
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…
COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation
Jiefeng Li, Ye Yuan, Davis Rempe +5
Estimating global human motion from moving cameras is challenging due to the entanglement of human and camera motions. To mitigate the ambiguity, existing methods leverage learned…
GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
Ye Yuan, Xueting Li, Yangyi Huang +4
Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek…
PACE: Human and Camera Motion Estimation from in-the-wild Videos
Muhammed Kocabas, Ye Yuan, Pavlo Molchanov +5
We present a method to estimate human motion in a global scene from moving cameras. This is a highly challenging task due to the coupling of human and camera motions in the video.…