3 citations · 5 across the 16 of their papers we have counts for
20 papers
InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation
Yucheng Zhang, Sirui Xu, Jinhong Li +8
Captured human-object interactions provide rich supervision for humanoid loco-manipulation, but they are sparse, heterogeneous, and not directly executable by robots. We introduce…
DETRAM: End-to-end DEtection, Tracking and Recovery of HumAn Meshes
Chunggi Lee, Seonwook Park, Wanhua Li +2
In the task of human mesh recovery (HMR), multi-person scenes are particularly difficult to handle due to the many entities that appear and occlusions between them over time. In pa…
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…
D-Rex : Diffusion Rendering for Relightable Expressive Avatars
Timo Teufel, Xilong Zhou, Umar Iqbal +4
We present D-Rex, a person-specific framework for photorealistic, relightable, expressive, and animatable full-body human avatars with free-viewpoint rendering. Existing methods fo…
SOMA: Unifying Parametric Human Body Models
Jun Saito, Jiefeng Li, Michael de Ruyter +12
Parametric human body models are foundational to human reconstruction, animation, and simulation, yet they remain mutually incompatible: SMPL, SMPL-X, MHR, Anny, and related models…
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…
SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Zhengyi Luo, Ye Yuan, Tingwu Wang +25
Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid contro…
HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis
Timo Teufel, Pulkit Gera, Xilong Zhou +5
Simultaneous relighting and novel-view rendering of digital human representations is an important yet challenging task with numerous applications. Progress in this area has been si…
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…
VideoPanda: Video Panoramic Diffusion with Multi-view Attention
Kevin Xie, Amirmojtaba Sabour, Jiahui Huang +5
High resolution panoramic video content is paramount for immersive experiences in Virtual Reality, but is non-trivial to collect as it requires specialized equipment and intricate…
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…
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…
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
Alex Trevithick, Matthew Chan, Towaki Takikawa +5
3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of…
Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation
Mathis Petrovich, Or Litany, Umar Iqbal +4
Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prom…
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.…
Self-Supervised Object Detection via Generative Image Synthesis
Siva Karthik Mustikovela, Shalini De Mello, Aayush Prakash +5
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection. We use collections of real world imag…