22 citations · 63 across the 11 of their papers we have counts for
15 papers
Video Generation Models are General-Purpose Vision Learners
Letian Wang, Chuhan Zhang, Rishabh Kabra +9
Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models. What, then, is the equivalent catalyst needed to achieve a genera…
Spectral Coherence Index: A Model-Free Metric for Protein Structural Ensemble Quality Assessment
Yuda Bi, Huaiwen Zhang, Jingnan Sun +1
Protein structural ensembles from NMR spectroscopy capture biologically important conformational heterogeneity, but it remains difficult to determine whether observed variation ref…
DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans
Akash Sengupta, Thiemo Alldieck, Nikos Kolotouros +3
We present DiffHuman, a probabilistic method for photorealistic 3D human reconstruction from a single RGB image. Despite the ill-posed nature of this problem, most methods are dete…
VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
Enric Corona, Andrei Zanfir, Eduard Gabriel Bazavan +3
We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models. Our m…
SPHEAR: Spherical Head Registration for Complete Statistical 3D Modeling
Eduard Gabriel Bazavan, Andrei Zanfir, Thiemo Alldieck +3
We present \emph{SPHEAR}, an accurate, differentiable parametric statistical 3D human head model, enabled by a novel 3D registration method based on spherical embeddings. We shift…
Blendshapes GHUM: Real-time Monocular Facial Blendshape Prediction
Ivan Grishchenko, Geng Yan, Eduard Gabriel Bazavan +5
We present Blendshapes GHUM, an on-device ML pipeline that predicts 52 facial blendshape coefficients at 30+ FPS on modern mobile phones, from a single monocular RGB image and enab…
DreamHuman: Animatable 3D Avatars from Text
Nikos Kolotouros, Thiemo Alldieck, Andrei Zanfir +3
We present DreamHuman, a method to generate realistic animatable 3D human avatar models solely from textual descriptions. Recent text-to-3D methods have made considerable strides i…
HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving
Andrei Zanfir, Mihai Zanfir, Alexander Gorban +4
Autonomous driving is an exciting new industry, posing important research questions. Within the perception module, 3D human pose estimation is an emerging technology, which can ena…
Structured 3D Features for Reconstructing Controllable Avatars
Enric Corona, Mihai Zanfir, Thiemo Alldieck +3
We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, sta…
BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation
Ivan Grishchenko, Valentin Bazarevsky, Andrei Zanfir +7
We present BlazePose GHUM Holistic, a lightweight neural network pipeline for 3D human body landmarks and pose estimation, specifically tailored to real-time on-device inference. B…
HSPACE: Synthetic Parametric Humans Animated in Complex Environments
Eduard Gabriel Bazavan, Andrei Zanfir, Mihai Zanfir +3
Advances in the state of the art for 3d human sensing are currently limited by the lack of visual datasets with 3d ground truth, including multiple people, in motion, operating in…
THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers
Mihai Zanfir, Andrei Zanfir, Eduard Gabriel Bazavan +3
We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images. Key to our methodology is an inte…
Neural Descent for Visual 3D Human Pose and Shape
Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Zanfir +3
We present deep neural network methodology to reconstruct the 3d pose and shape of people, given an input RGB image. We rely on a recently introduced, expressivefull body statistic…
Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows
Andrei Zanfir, Eduard Gabriel Bazavan, Hongyi Xu +3
Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervis…
Human Synthesis and Scene Compositing
Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa +2
Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important…